LGN Activity Patterns during Ocular Dominance Plasticity in vivo

By

Monica L. Linden

S.B. Mathematics with Computer Science,

S.B. Brain and Cognitive Sciences

Massachusetts Institute of Technology, 2002

SUBMITTED TO THE DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES IN

PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY IN NEUROSCIENCE

AT THE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

JUNE 2008

© Massachusetts Institute of Technology

All rights reserved

Signature of Author: ____________________________________________________________

Department of Brain and Cognitive Sciences

May 13, 2008

Certified by: ___________________________________________________________________

Mark F. Bear, Ph.D.

Picower Professor of Neuroscience

Thesis Supervisor

Accepted by: __________________________________________________________________

Matthew A. Wilson, Ph.D.

Professor of Neurobiology

Chairman, Committee for Graduate Students

LGN Activity Patterns during Ocular Dominance Plasticity in vivo

By

Monica L. Linden

Submitted to the Department of Brain and Cognitive Sciences

on May 13, 2008 in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy in Neuroscience

ABSTRACT

Perturbations of sensory experience in young animals are known to cause lasting changes in

adult brain function. For example, monocular visual deprivation by lid closure (MC) leads to a

loss of cortical responsiveness of the deprived eye and a concomitant visual impairment. This

ocular dominance (OD) plasticity is a well-studied model of experience-dependent cortical

plasticity. While much is known about the anatomical, physiological and biochemical changes

that occur in primary visual cortex following OD plasticity, the input patterns that lead to these

changes have not been characterized.

Visual input travels from the retina through the dorsal lateral geniculate nucleus (dLGN) of the

thalamus and then into visual cortex. Several models of the thalamic activity patterns which

drive OD plasticity have been proposed, but the assumptions about the pattern and amount of

input activity from thalamus to cortex during deprivation have not been experimentally

validated. Therefore, we performed extracellular recordings from the dLGN of animals during

periods of visual manipulation.

Contrary to previous hypotheses, the present findings demonstrate that MC does not alter the

overall firing rate of neural activity in the dLGN. Instead, MC alters the pattern of neural spike

trains such that there is a decrease in simultaneous firing of neighboring neurons. Moreover, the

elimination of visual input from the retina, a form of deprivation which does not lead to

deprived-eye depression, leads to a dramatic increase in thalamic bursting. Additionally, there

are subtle qualitative differences between dLGN activity in juveniles and adults during MC, and

this may contribute to differences in OD plasticity with age. These findings substantially alter

the interpretation of previous studies and define the activity patterns that govern cortical

plasticity in vivo. Furthermore, this work may have important implications for treatments of

developmental disorders including ambylopia.

Thesis Supervisor: Mark F. Bear, Ph.D.

Title: Picower Professor of Neuroscience

Monica L. Linden mlinden@alum.mit.edu

Education: Massachusetts Institute of Technology Doctor of Philosophy – June, 2008

Cambridge, MA

Ph.D. in Neuroscience, Department of Brain and Cognitive Sciences.

Major: Systems Neuroscience, Minor: Computational Neuroscience.

Massachusetts Institute of Technology Bachelor of Science – June, 2002

Cambridge, MA

Bachelor of Science degrees in Mathematics with Computer Science and Brain and

Cognitive Sciences, Minor: Ancient and Medieval Studies.

Awards/

Honors: NIH National Research Service Award Predoctoral Fellowship (2007)

Walle Nauta Award for Continuing Dedication to Teaching (2006)

Dean’s Educational and Student Advising Award (2005)

Agnus MacDonald Award for Excellence in Undergraduate Teaching (2005)

National Science Foundation Graduate Research Fellowship (2002)

MIT Brain and Cognitive Sciences Award for Academic Excellence (2002)

Phi Beta Kappa Honor Society

Sigma Xi Research Honor Society

Teaching

Experience: MIT 9.01: Introduction to Neuroscience – Head TA Fall 2004, Fall 2005

Taught one weekly section (~25 students) under Profs. Mark Bear and Sebastian Seung.

Coordinated TAs, multiple guest lecturers, and the writing staff.

MIT 9.00: Introduction to Psychology – TA Fall 2003

Taught two recitation sections (~25 students) under Prof. Jeremy Wolfe.

MIT Orientation for Graduate Teaching Staff – Workshop Facilitator

Fall 2005

Co-facilitated “Problems, Pitfalls, Booby Traps and Surprises (in Teaching)” workshop

during the fall TA orientation, sponsored by the Teaching and Learning Lab.

Research

Experience: MIT Picower Center for Learning and Memory – Bear Lab

Cambridge, MA June, 2003 – 2008

Graduate Student Researcher: Performing in vivo electrophysiology in awake rodents to

understand various aspects of neural plasticity in the visual system.

National Security Agency – Students' Summer Program Government Communications Headquarters Cheltenham, UK

Summer, 2002

Intern: One of two student liaisons selected to represent the US at this British security

agency. Developed mathematical models to analyze systems. Prepared a research report

for internal publication and presented results to internal mathematics community.

MIT Picower Center for Learning and Memory – Tonegawa Lab

Cambridge, MA September, 2001 – March, 2002

Undergraduate Researcher: Data analysis for electrophysiological studies in genetically

manipulated mice.

National Security Agency – Director's Summer Program

Ft. Meade, MD Summer, 2001

Intern: Developed mathematical models to analyze and evaluate data. Prepared a

research report for internal publication and briefed the Director of the Agency. Top

Secret Clearance.

Consulting: Lippincott Williams and Wilkin – Subject Matter Expert Fall 2005

Reviewed manuscripts of ancillary material, specifically “Student Assignments and

Activities” for Neuroscience, Exploring the Brain, 3rd Edition by Bear, Connors and

Paradiso. This is the textbook used in 9.01 – Introduction to Neuroscience, co-authored

by Prof. Bear.

Publications &

Presentations: M.L. Linden, A.J. Heynen, R. Haslinger, M.F. Bear. (2008) Thalamic activity that drives

visual cortical plasticity. Submitted

M.L. Linden, A.J. Heynen, R. Haslinger, E.N. Brown, M.F. Bear. LGN activity patterns

during ocular dominance plasticity. Picower-RIKEN Symposium, November 8 – 9, 2007.

Massachusetts Institute of Technology, Cambridge, MA. Poster Presentation.

M.L. Linden, A.J. Heynen, R. Haslinger, E. N. Brown, M. F. Bear. LGN activity

patterns during ocular dominance plasticity. Society for Neuroscience, Abstracts,

November 3 – 7, 2007. Society for Neuroscience Annual Meeting, San Diego, CA.

Poster Presentation.

M.L. Linden. LGN activity patterns during ocular dominance plasticity. MIT Brain

Lunch Seminar Series, October 15, 2007. Massachusetts Institute of Technology,

Cambridge, MA. Lecture.

M.L. Linden. LGN activity patterns during ocular dominance plasticity. Picower

Institute for Learning and Memory, Annual Retreat, May 30 – June 1, 2007. Cape Cod,

MA. Lecture.

M.G. Shuler, M.L.Linden, M.Bear. (2004) Learning induced, persistent reward-related

activity in the primary visual cortex of adult rodents. Society for Neuroscience,

Abstracts, October 23 – 27, 2004. Society for Neuroscience Annual Meeting, San Diego,

CA.

Acknowledgments

“We were but stones, your light made us stars”

–Low Light, Pearl Jam

There are numerous people whom I’d like to thank, without which my thesis project would not

have been as successful. First of all, I’d like to thank Mark Bear. I thoroughly enjoyed working on the

fundamental problem which he gave to me, and I was constantly driven to work through my frustrations

because I knew there had to be answers. I appreciate all his support throughout the project, even after I

upended his hypotheses. I would also like to thank the rest of my thesis committee for their helpful

insights: Mriganka Sur, Matthew Wilson and especially Harvey Swadlow, who responded patiently to my

emails even when I sent several in the same day. I’d also like to acknowledge my funding sources

including a graduate research fellowship from NSF and an NRSA predoctoral fellowship from

NINDS/NIH.

The Bear Lab provided an incredibly supportive environment throughout my graduate career.

Arnie Heynen was a constant source of scientific expertise and emotional support. I couldn’t have done

this without him. Marshall Shuler patiently taught me how to be an electrophysiologist and was always

there to help me think through my ideas and to challenge my assumptions. Jason Coleman was always

willing to help me and provided invaluable knowledge of the dLGN. Sharing lab space with Jonathan

Whitlock meant my day in lab would be much more fun. Suzanne Meagher always took care of me, and

Erik Sklar fixed everything I managed to break. Our technicians, Caroline Dudley, Cindy Poo, Tina

Udaka, and especially Kathleen Oram who did my histology, were wonderful. Gordon Smith always had

great answers to questions I asked him, even when they had nothing to do with what he was studying, and

even when I asked him quietly during lab meeting. Misha Frenkel, Gül Dölen, Sam Cooke and Emily

Osterweil were great sounding-boards for discussing my problems. Lena Khibnik, Rahmat Muhammad,

and Wendy Chen were all wonderful friends inside and outside the lab, and really helped me to get

through these past few years. I love you, ladies! And I’d like to thank the rest of the Bear lab members

with whom I’ve worked – all of you have had a positive impact on my work and my graduate school

experience.

I’d also like to thank my collaborator, Rob Haslinger, for his assistance with the data analysis,

Brian Blais and Harel Shouval for their insight and assistance interpreting my results, and Mike Long for

helpful conversations regarding my project.

I have to thank Pearl Jam for the 7 concerts I attended in graduate school, and the countless hours

I spent listening to their music during surgeries, experiments, and data analysis.

I couldn’t have accomplished this without the support of my friends. I’d like to thank Gabrielle

Cayton, Wendy Chen, and Sumiko Mekaru for being my fabulous roommates, and their cats Coco, Juliet

and Cobweb, who provided much-needed affection after many stressful days in lab. I’d also like to thank

Melissa Thirer, who has known me my entire life, and has always been there to support me, and Kevin

Matulef for going through all the ups and downs of graduate school with me. In addition to the rest of my

friends in Boston, I’d like to thank my long-distance friends, especially Naomi Eckmann, Shantonu Sen,

Seth Bisen-Hersh, Brian Bucklew, Michael Korn, Dan Winters and Craig Citro, who are never more than

an IM message, email, or phone call away.

Finally, I’d like to thank my family who has always supported whatever I’ve chosen to do. My

grandmothers, Florence and Josette, are a constant source of inspiration and unconditional love. And my

Mom is always there for me, no matter what. I love you all. I’d like to dedicate this thesis to my Dad,

who always knew I could accomplish this, even from the time when my greatest scientific achievement

was comparing fresh squeezed and concentrated orange juice.

Table of Contents

Chapter 1 ..................................................................................................................................... 11

Introduction ............................................................................................................................... 13

dLGN Anatomy ......................................................................................................................... 14

dLGN Physiology ...................................................................................................................... 19

Ocular Dominance Plasticity ..................................................................................................... 29

References ................................................................................................................................. 41

Summary ................................................................................................................................... 50

Chapter 2 ..................................................................................................................................... 51

Abstract ..................................................................................................................................... 53

Introduction ............................................................................................................................... 54

Methods Summary .................................................................................................................... 55

Results ....................................................................................................................................... 56

Discussion ................................................................................................................................. 59

Detailed Methods ...................................................................................................................... 85

References ................................................................................................................................. 89

Chapter 3 ..................................................................................................................................... 91

Abstract ..................................................................................................................................... 93

Introduction ............................................................................................................................... 94

Methods Summary .................................................................................................................... 95

Results ....................................................................................................................................... 96

Discussion ................................................................................................................................. 98

Detailed Methods .................................................................................................................... 113

References ............................................................................................................................... 116

Chapter 4 ................................................................................................................................... 119

Methodological Considerations ............................................................................................... 121

Implications ............................................................................................................................. 125

Future Experiments ................................................................................................................. 131

References ............................................................................................................................... 141

Appendix .................................................................................................................................... 145

Appendix 1 .............................................................................................................................. 147

Appendix 2 .............................................................................................................................. 150

References ............................................................................................................................... 155

11

Chapter 1

INTRODUCTION AND BACKGROUND

13

Introduction

The dorsal lateral geniculate nucleus (dLGN) of the thalamus transmits visual

information from the retina to the visual cortex allowing for visual perception and serving the

“what” and “where” streams of visual information processing. While textbooks often define

thalamic nuclei as mere relay stations between the periphery and the cortex, recent evidence

suggests that important processing takes place within the thalamus (reviewed in Sherman, 2007).

Because the dLGN acts as the gateway into visual cortex, it is important to understand not only

the anatomy and circuitry of this brain area, but also how the dLGN transforms and transmits

retinal information en route to the visual cortex. Therefore, we will begin by reviewing both

dLGN circuitry and dLGN activity. We will draw from studies conducted on several different

species including cats, ferrets, rats and mice. Although the mouse served as the animal model

for the experiments described in the body of this thesis, many of the findings on which the study

is based were discovered in other species (e.g. Rittenhouse et al., 1999). Furthermore, the

literature on the mouse dLGN is not comprehensive, and by including the literature from other

species and assuming that the basic principles are preserved, we can better understand the system

under investigation.

Our interests lie in understanding the role of dLGN input to the visual cortex during

visual manipulations that induce experience-dependent plasticity. Therefore, in addition to

understanding the dLGN, this literature survey will discuss monocular deprivation and the

resulting ocular dominance plasticity, which will serve as our paradigm for exploring

experience-dependent plasticity in the cortex.

14

dLGN Anatomy

The dLGN is contained within the dorsal thalamus, the largest part of the mammalian

diencephalon. Developing from the region between the epithalamus and ventral thalamus, all

dorsal thalamic nuclei have relay cells that project to a region of neocortex in the telencephalon

(Sherman and Guillery, 2006). Specifically, the dLGN is a first-order thalamic relay transmitting

visual information from both retinae to the visual cortex ipsilateral to the nucleus.

The processing that occurs in any region of the brain is contingent on both the inputs to

that brain area and the connections and circuits within the area. In order to understand the

processing that occurs in the dLGN, we must begin by exploring the structural properties of this

brain region. There are two main categories of neurons in the dLGN: relay cells and

interneurons. Rodents lack interneurons in some thalamic nuclei including the ventrobasal

complex and the ventrolateral nucleus (Arcelli et al., 1997). However, using Golgi staining

techniques and electron microscopy, two types of neurons were identified in the mouse dLGN

(Rafols and Valverde, 1973). The larger category of neurons was later identified as the

excitatory relay cells, with the second category, identified as inhibitory interneurons, comprising

15 – 20% of the neuronal population (Fig. 1-1a) (Arcelli et al., 1997).

In several species it is possible to further classify relay cells based on properties including

cell size, axonal or dendritic arbor properties, axon diameter and/or conduction velocity, and

receptive field (RF) properties. Using physiological classifications originally established in the

retina (Enroth-Cugell and Robson, 1966) and later extended to the dLGN (Cleland et al., 1971)

and morphological descriptions established through Golgi techniques (Guillery, 1966), LeVay

and Ferster (1977) provided the first description of morphologically and functionally distinct

relay cell types in the cat. By combining electrophysiological recordings with horseradish

15

peroxidase labeling, Friedlander et al. (1979) directly compared cell function with morphology,

later refining the criteria to segregate cat relay cells into W-, X- and Y-cell categories based on

both morphological and electrophysiological properties (Friedlander et al., 1981).

X-cells have smaller cell bodies than Y-cells, with elongated dendritic trees that remain

in a single dLGN layer. Y-cells have spherical dendritic arbors that can cross between layers.

With thicker dendrites, Y-cells have faster conduction velocities than X-cells. Physiologically,

the larger dendritic arbors of Y-cells lead to larger RFs. Additionally, Y-cells integrate inputs in

a nonlinear fashion, whereas X-cells spatially summate linearly. W-cells are distinguished from

X- and Y-cells based on their location within dLGN (Friedlander et al., 1981).

W-, X- and Y-cell types (and their primate equivalents of konicellular, parvocellular, and

magnocellular cell types, respectively) have been identified in the dLGN of numerous species

including two rodents: the squirrel (Van Hooser et al., 2003) and the rat both morphologically

(Brauer et al., 1979) and physiologically (Lennie and Perry, 1981; Gabriel et al., 1996).

However, there are no studies in the mouse to indicate the presence of distinct relay cell types.

Additionally, electrophysiological recordings from the mouse have shown no indication of

nonlinearly summing Y-cells (Grubb and Thompson, 2003). While the lack of evidence for Y-

cells does not imply that they are not present in the dLGN of mice, they may have a more

homogeneous dLGN than other “higher-order” species.

Another anatomical feature of the dLGN is its layered structure. These layers can be

distinguished by several properties including relay cell type, on- versus off-center RFs and the

eye of origin contributing retinal input. The functional role of the layers is unknown. In fact,

different species show a huge variety in the types of properties which distinguish the layers. For

example, X- and Y-cell types are intermingled in the A and A1 laminae of the cat dLGN, but

16

segregate into distinct layers in the squirrel. This may imply that there is no functional advantage

to the layering (Sherman and Guillery, 2006). Instead, it appears that the properties of the

different layers emerge entirely from the properties of the retinal input (Fukuda and Stone, 1974;

Sur et al., 1987). Additionally, interneurons can modulate input between layers (Sherman and

Spear, 1982) and intracellular recordings of dLGN neurons have shown that the large majority of

cells respond to input presented to either eye (Zhou et al., 2003) , thus layers do not serve as

distinct processing areas within the dLGN. Another indication that the layers may be

unimportant arises from the observation that cortical layers mix the input that remains distinct in

dLGN, for example eye of origin or X- and Y-cells.

Rats and mice do not appear to have layers based on relay cell type or on/off RF

structure, however a “hidden lamination” exists within the rat (Reese, 1988) and mouse dLGN

(Godement et al., 1984; Muir-Robinson et al., 2002; Jaubert-Miazza et al., 2005) with the inputs

from the two eyes remaining separate (Fig. 1-1). The outer “shell” of the dLGN receives

exclusive input from the contralateral eye, surrounding an inner “core” of ipsilateral input. The

segregation of eye-specific input into dLGN has been shown to be an activity dependent,

developmental process in cats and ferrets (Shatz, 1996; Weliky and Katz, 1999; Crowley and

Katz, 2002), as well as in mice (Jaubert-Miazza et al., 2005; Hooks and Chen, 2006).

In order to understand the processing that occurs in the dLGN it is also necessary to

discuss the input to the circuit. Thalamic afferents can be divided into two categories: drivers

and modulators. Drivers determine the qualitative RF properties of the relay cells, thus the

retinal ganglion cell (RGC) input serves as the driving input to dLGN, imbuing dLGN neurons

with RFs responsive to one eye in a center-surround configuration. While the driving input is

most important in determining the response properties of the thalamic relay cells, they comprise

17

the minority of synapses (Van Horn et al., 2000). The majority are modulators which affect the

quantitative aspects of the relay cell response including overall firing rate and size and strength

of the center and surround. Geniculate relay cells receive modulating input from several sources

including corticothalamic feedback, local interneurons, the thalamic reticular nucleus (TRN), and

brainstem.

The driving input to thalamic nuclei defines the information that will pass from the driver

to the cortex. The visual cortex is visual precisely because driving input from the retina passes

through the dLGN to this region of the brain. Rewiring a driver from a different modality into a

thalamic nucleus leads to neurons in cortex with RFs reflecting the driving input and can lead to

behaviors dependent on the new input. For example, rewiring visual input into the auditory

thalamus results in visual RFs in auditory thalamus and cortex and can result in visually driven

behaviors dependent on this rewired pathway (Sur et al., 1988; Sharma et al., 2000; von

Melchner et al., 2000).

The synapses formed by the drivers support the observation that they provide the

strongest input to the dLGN. Originally described by Guillery (1966), these large synapses are

localized to the terminal zones, and exhibit closely packed boutons. These findings were

confirmed by electron microscopy (EM) which shows that the vesicles at the RGC synapses are

round and contained in large synapses (RL synapses) (Wilson et al., 1984).

Each modulatory input to the dLGN has a unique EM profile. The corticothalamic

feedback, arising from layer VI of visual cortex (Gilbert and Kelly, 1975), forms short terminals

on the side branches of dLGN dendrites (Guillery, 1966). The synapses are small and contain

round vesicles (RS) (Erisir et al., 1997a). This feedback is topographic (Murphy and Sillito,

1996). Eliminating cortical input has been shown to have a small effect on dLGN neurons,

18

affecting synchrony between neurons and also the surround antagonism (Sillito et al., 1994;

Cudeiro and Sillito, 1996). However, the effect of corticothalamic feedback may be stronger in

an awake preparation (Sherman and Guillery, 2006).

The TRN sends γ-aminobutyric acid (GABA)-ergic afferents to dLGN (Houser et al.,

1980). In the rat, the terminals form grape-like boutons (Pinault et al., 1995). In the cat, EM

studies show the synapses are symmetric, and contain flattened vesicles (F1 synapses) (Wang et

al., 2001). These inputs are topographic, and may be important during sleep and for modulating

attentional states (Wang et al., 2001).

The local interneurons within the dLGN are also GABA-ergic and also display F1

synapses along their axons (Montero, 1987). However, the distal dendrites of local interneurons

also form synapses with the relay cells, and these synapses are characterized by their F2 profile

(Montero, 1986). F2 synapses have a more random distribution of vesicles than found at F1

terminals.

There are several additional sources of modulatory input to the dLGN including

cholinergic, serotonergic, dopamenergic and noradrenergic inputs from the brainstem and

hypothalamus. Cholinergic input accounts for 30% of the input into the dLGN (Erisir et al.,

1997b; Erisir et al., 1997a). This input forms RS terminals close to the relay cell bodies (Erisir et

al., 1997b).

While relay cells do receive input from all of these sources, some synapses within the

dLGN form a specialized architecture known as a “triad” (reviewed in Sherman, 2004). These

triads are sites where 3 or more synaptic profiles are found very close together, and the synapses

are contained within a glomerulus. A glomerulus is a site free of astrocyctic cytoplasm and

enclosed by an astrocytic sheath (Szentagothai, 1963; Sherman and Guillery, 2006). A triad

19

forms when an RL driving synapse is presynaptic to two sites: (1) a relay cell dendrite and (2) an

interneuron dendrite at an F2 terminal. This F2 terminal on the interneuron is in turn,

presynaptic to the same relay cell dendrite as (1). While corticothalamic feedback and TRN

innervation do not generally occur within glomeruli, cholinergic input is often found there (Erisir

et al., 1997b). This anatomy immediately suggests that the input from the RGCs is modified by

the dLGN, as the relay cell receiving this input is inhibited by an interneuron receiving the same

input, one synapse later. However, the protection of the glial sheath and the absence of

astrocytes at the synapse may allow neurotransmitters to move more freely at these synapses,

rendering them more faithful to their input (Sherman and Guillery, 2006). Furthermore, the

cholinergic input is able to directly modify this retinogeniculate transmission.

Triads exist in the dLGN of several species including cats and rats. While it appears that

only X-cell types are found in cat triads, this may not be the case in rat triads (Lam et al., 2005).

Similar studies have not yet been conducted in the mouse, although early studies using electron

microscopy in the mouse dLGN suggest triads may exist in this species (Rafols and Valverde,

1973).

dLGN Physiology

In addition to understanding the anatomy of dLGN circutiry, it is important to understand

the physiological properties of geniculate neurons and how they transform messages from the

retina to V1. Grubb and Thompson (2003) have characterized the basic response properties of

dLGN neurons in the mouse. Neurons in the mouse dLGN have circular RFs with the classical

center-surround structure found in higher mammals. The RFs have strongly responsive central

regions with diameters of 4 – 20° and weaker surrounds. The median peak spatial frequency for

mouse dLGN neurons is 0.03 cycles/degree, and the mean peak temporal frequency is

20

approximately 4 Hz. While the spatial resolution of the mouse dLGN is much lower than that of

more visual species, the peak temporal frequency is comparable to many species including

humans. Mouse geniculate neurons predominantly sum spatial input in a linear fashion

indicating that mouse dLGN neurons are X-like (Grubb and Thompson, 2003). There is no clear

transformation of the RGC RF properties in the dLGN. This suggests that the role of the dLGN

is to gate and/or modulate retinal input (Sherman and Guillery, 2006).

Retinogeniculate Transmission

Even though the dLGN does not change the basic RF properties transmitted from the

retina, it is possible for the dLGN to transform the message sent from the retina to V1 by

modulating the spike train. Theoretically, there are several ways that the dLGN can change the

retinal input. For example, the spike train originating in the RGCs could have spikes added or

removed, or the temporal patterning of the spikes could be modified before the message is sent to

cortex.

The first location where the visual signal leaving the retina can be altered is at the

retinogeniculate synapse. Simultaneous recordings from RGCs and dLGN neurons have allowed

for studies of the efficacy of the retinogeniculate pathway. The efficacy of retinogeniculate

transmission is defined as the probability that an RGC spike will evoke a spike in a

monosynaptically connected dLGN neuron. dLGN neurons fire approximately 5 ms after RGC

spiking in the anesthetized cat preparation (Mastronarde, 1987). The efficacy of synaptic

transmission from RGC to dLGN is relatively low (approximately 3%) (Usrey et al., 1998).

However, the efficacy is enhanced by short preceding interspike intervals (ISIs) of 3 – 30 ms

(Mastronarde, 1987; Usrey et al., 1998; Rowe and Fischer, 2001). The length of the ISI that will

still result in increased efficacy of the second spike is modulated by the overall firing rate of the

21

RGC (Rowe and Fischer, 2001). Levine and Cleland (2001) further described the efficacy of this

synapse by classifying all recorded dLGN spikes into those evoked from RGC stimuli, and those

that arose from an “anonymous” (non-retinal) source, while also considering if the recorded

RGC spikes were effective at driving the dLGN. They confirmed that short preceding ISIs

enhance the efficacy of RGC transmission, with efficacy dropping off exponentially with ISI

length. They also found that very short ISIs from the RGCs (< 3ms) lead to a refractory period

for dLGN firing. Additionally, if the ISI between a failed RGC input and an anonymous input is

short, the anonymous input will be more likely to lead to a dLGN response. However,

anonymous inputs tend to decrease the likelihood of an RGC input triggering a dLGN response,

possibly due to refractoriness, although they observe no refractory effect when dLGN spikes are

not triggered by RGCs (Levine and Cleland, 2001). Additionally, visual stimulation enhances

the efficacy of the retinogeniculate synapse, perhaps because of the increased synchrony in

RGCs projecting to the same dLGN neuron during vision (Usrey et al., 1999; Rowe and Fischer,

2001)

It is also possible to study retinogeniculate transmission with only a single recording

electrode in the dLGN. Two recent studies have used the method of S-potential recordings to

look at transmission at this synapse in the awake cat (Weyand, 2007) and monkey (Sincich et al.,

2007). S-potentials refer to the retinal excitatory postsynaptic potentials (EPSPs) which can also

be recorded extracellulary from dLGN neurons. Using this technique, they show that the second

EPSP is more likely to evoke a dLGN spike if it was preceded by an EPSP followed by a short

inter-potential interval, confirming the paired retinal and dLGN findings. It was also shown that

during wakefulness, the interval that leads to facilitated transmission is shorter than under

anesthesia, although general efficacy is improved under awake conditions (Weyand, 2007).

22

Slice experiments have also contributed to our understanding of the transformations that

can occur at the retinogeniculate synapse. Using slices prepared from postnal day (p)28 – p33

mice, dLGN neurons were recorded in a dynamic clamp mode during optic tract stimulation

(Blitz and Regehr, 2003). This allowed for a comparison of the α-amino-3-hydroxy-5-methyl-4-

isoxazolepropionic acid (AMPA) and N-methyl-D-asparate (NMDA) components of the dLGN

response. The AMPA component allowed for a precise and faithful following of the RGC input

with short latency responses. However, this component was sensitive to short-term plasticity in

the forms of both presynaptic depression and AMPA receptor (AMPAR) desensitization. The

NMDA component acted at a longer latency and was less precise, but allowed for multiple

dLGN responses and an amplification of the RGC input. Depending on the strength of the

NMDA and AMPA components of the dLGN response, a single dLGN neuron will produce a

reliable response to a repeated pattern of input, with the two components working together to

modulate the response relayed to V1 (Blitz and Regehr, 2003).

Modulation of the Retinogeniculate Synapse

The retinogeniculate synapse is the first location where visual input can be modified on

its path to cortex, and the degree of change that occurs at this synapse can be modulated by input

from other brain areas. Using whole-cell patch-clamp recording methods and calcium imaging,

it has been shown that dopamine (D) (Govindaiah and Cox, 2006) serotonin (5-HT) and GABA

(Chen and Regehr, 2003) can inhibit synaptic strength and decrease synaptic depression. These

neuromodulators can act presynaptically through D2 (Govindaiah and Cox, 2006), 5-HT or

GABAB receptors (Chen and Regehr, 2003) or postsynaptically through GABAA receptors

(McCormick and Bal, 1994).

23

In vivo studies have shown that eye movements can also modulate the dLGN response

(Lal and Friedlander, 1990) as shown in the paralyzed, anesthetized cat preparation using passive

eye movement. Additionally, electrical stimulation of the parabrachial region of the brain stem

leads to an increased release of acetylcholine in the dLGN. This cholinergic activation has been

shown to facilitate the response of both X- and Y-cell types to sinusoidal gratings (Uhlrich et al.,

1995).

Geniculocortical Transmission

In order to understand the plasticity that occurs in visual cortex, it is necessary to

consider not only the input sent from the retina to the dLGN, but also the message transmitted

across the thalamocortical (TC) synapse and received by the cortical neuron. The rules

governing the plasticity at the TC synapse most likely rely on both the input to the cortical

neuron and its response. As discussed above, the message from retina to the dLGN is modified

at the retinogeniculate synapse, and it is not necessarily a simple, faithful relay of visual

information. Below I will describe how visual information is transformed at the TC synapse.

TC transmission is often studied using simultaneous recordings from the dLGN and V1

in a manner similar to studies of retinogeniculate transmission. Alonso et al. (2001) described

the properties shared by connected dLGN and V1 simple cells in the anesthetized cat

preparation. They sampled from numerous pairs of dLGN and V1 simple cells with overlapping

RFs. However, only one-third of their pairs were connected. Neurons are said to be connected if

they have overlapping RFs, with the dLGN spikes preceding the V1 spikes by 2 – 4 ms (Tanaka,

1983). Other RF properties proved important in predicting whether a pair of neurons was

connected. Connected neurons generally had the same RF sign and size and were not just

overlapping in space (Alonso et al., 2001).

24

Efficacy studies similar to those conducted at the retinogeniculate synapse have been

performed at the TC synapse as well. A single spike preceded by at least 10 ms of silence has

about a 1% chance of driving a connected cortical partner (Usrey et al., 2000). If two spikes

arrive within a 10 – 15 ms window, the second spike will show an enhancement in efficacy,

becoming up to 5 times more likely to drive the postsynaptic neuron (Usrey et al., 2000). Usrey

et al. (2000) were also able to record simultaneously from two dLGN neurons and a V1 neuron

to which they were both connected. In this case, if spikes arrived from the two neurons within

7.5 ms, the second spike to arrive showed an enhancement in efficacy.

Seemingly contrary to the increased efficacy of the second spike, other studies have

shown that the TC synapse is depressing in vivo (Jia et al., 2004; Boudreau and Ferster, 2005).

Field potential recordings from rat V1 during electrical stimulation of dLGN show paired-pulse

depression (PPD), with the degree of depression increasing as the frequency of the stimulation

increases (Jia et al., 2004). This short-term synaptic depression is dependent on GABA-

mediated inhibition, and is not affected by NMDA or AMPA receptor blockers. Intracellular

recordings during dLGN stimulation in cat show a decrease in postsynaptic potentials in neurons

monosynaptically receiving dLGN input following repeated stimulation (Boudreau and Ferster,

2005). This depression was not affected by cortical stimulation. However, reducing dLGN

activity (via increased intraocular pressure) greatly enhanced the first evoked PSP, although

subsequent PSPs were depressed. These observations imply that the TC synapse may be in a

state of tonic depression due to spontaneous input, and decreasing that input reduces the

depression. However, the authors who conducted the study note that the degree of PSP

depression is small (never more than 40%). From this they conclude that amount of depression

based on spontaneous dLGN activity alone is near saturation, and has little effect on the efficacy

25

of transmission at this synapse. Further evidence for the tonic depression of the TC response

comes from work in the somatosensory system of the awake rabbit. Using current source-

density analysis, Swadlow et al. (2002) found an increased cortical response when the TC input

was preceded by a long ISI. The long ISI would allow time for the release from tonic

depression, thus enhancing the response.

Even though the depression is small and can be overcome, it is difficult to reconcile this

PPD and PSP depression with the short-latency facilitation observed by Usrey et al. (2000). One

possibility may be choice of anesthesia. As opposed to Swadlow et al. (2002) who recorded

from awake animals and Jia et al. (2004) who used urethane anesthesia, Usrey et al. (2000) used

a barbiturate anesthesia during their recordings. This may have resulted in a decrease in dLGN

spontaneous activity, thus relieving the tonic depression and allowing for facilitation of

transmission at the TC synapse. However, Boudreau and Ferster (2005) also used barbiturates

and observed PSP depression. The difference in this case may be that their results are based on

electrical stimulation, not intrinsic dLGN activity, thus changes to dLGN firing based on

barbiturate anesthesia may not have a large effect on their results. It is important to note that

many of these studies of spike efficacy in the retinogeniculocortical pathway were performed in

the anesthetized, paralyzed preparation and factors such as attentional state, level of arousal, eye

movements, etc. may all influence transmission in the visual pathway. This underscores the

importance of recording from the awake animal.

One important difference between the retinogeniculate and geniculocortical pathways is

that while the retinal response is always precisely and tightly coupled to the dLGN response,

there is much more variability in the cortical response time relative to the dLGN input (Veredas

et al., 2005). Using a theoretical model, Veredas et al. proposed that the difference is due to

26

changes in the EPSP through the visual pathway. Increased EPSP rise time, duration and jitter in

the TC synapse relative to the retinogeniculate synapse can explain the differences in

correlations. While it may seem counterintuitive that the visual system would want to lose the

precise timing as information ascends the visual pathway, the increase in EPSP duration allows

for increased temporal summation at the V1 neuron, which may be important for visual

computation.

Disynaptic Connections

Kara and Reid examined the direct influence of RGCs on cortical firing by

simultaneously recording from RGCs and V1 neurons with overlapping RFs that were

disynaptically connected (2003). They found that a single RGC can account for about 3% of a

V1 neuron’s response, and that the V1 response is enhanced when the RGC spike is preceded by

a short (<10 ms) ISI. However, they found that this facilitation is greater than what would be

expected from a combination of the monosynaptic enhancements in the retinogeniculate and

geniculocortical synapses (Kara and Reid, 2003). This may imply that factors other than the

preceding ISI are important in determining spike efficacy. However, this study may also be

flawed by the use of barbiturate anesthesia (see above) and also the difficulty of assuring

connectivity between RGCs and the cortical neurons to which they project.

Bursts

The phenomenon of thalamic bursting is one important property of thalamic nuclei that

must be considered when evaluating TC transmission. While bursts are commonly observed

throughout the thalamus during sleep states, they have been shown to occur in the dLGN of the

awake cat (Guido and Weyand, 1995), rabbit (Bezdudnaya et al., 2006), and monkey

(Ramcharan et al., 2000). This section will focus on thalamic bursting during sensory

27

stimulation in order to understand how this type of activity could affect information transfer to

cortex.

Bursting results from the activation of T-type Ca2+

channels, which require a long period

of hyperpolarization for deinactivation (Sherman, 2001). Extracellularly, bursts are defined by a

period of quiescence lasting > 100 ms followed by two or more spikes with ISIs less than 4 ms

(Lu et al., 1992). The long silent period is the time in which the cell is hyperpolarized wherein

T-type channels become deinactivated. Following deinactivation, activating the channels

requires only a subthreshold increase in membrane potential. Once the channels are open, Ca2+

can enter the cell, depolarizing it even further to the Na+ channel activation potential, resulting in

a train of two or more spikes riding on the Ca2+

current.

Epochs of thalamic activity dominated by bursting are said to occur when the thalamus is

in a “burst mode”. Burst mode is highly correlated with slow-wave sleep, but also occurs in

awake, “inattentive” animals (Bezdudnaya et al., 2006). In recording from an awake animal,

periods of high bursting activity are accompanied by high voltage irregular hippocampal EEG

activity which is indicative of an inattentive state. Temporal tuning curves recorded from dLGN

neurons during alert versus inattentive states indicate that inattention (i.e. burst mode) flattens

the tuning curve and decreases response to visual stimuli. In this study, RF size remained the

same in both burst and tonic modes (Bezdudnaya et al., 2006). However, work performed in the

anesthetized cat dLGN has shown that burst spikes were restricted to smaller regions in the RF

center (Rivadulla et al., 2003). The discrepancy between these findings is likely to be accounted

for by anesthetic effects or differences in RF mapping methodology. This highlights the

importance of using an awake preparation when possible.

28

The purpose of bursts in signal transmission is unknown. Bursts are often triggered by

retinal spikes, with the first spike and secondary burst spikes being highly correlated with retinal

input (Usrey et al., 1999; Rowe and Fischer, 2001). Bursting may serve as a method of signal

amplification, as additional spikes in the burst are added to the RGC spike train to enhance the

message sent to V1. Larger visual stimuli are more likely to evoke bursts, which implies a role

for the inhibitory surround in burst production (Weyand et al., 2001). In fact, bursting, as

opposed to tonic firing, occurs following stimuli that strongly drive both the excitatory center

and inhibitory surround of the RF (Alitto et al., 2005).

dLGN bursts have also been shown to be related to visual processing. Bursts have been

shown to carry as nearly as much visual information as tonic firing (Reinagel et al., 1999).

Recordings from dLGN in awake cats show that the majority of bursts occur during visual

stimulation and are sometimes related to eye movements (Guido and Weyand, 1995). In the

anesthetized cat preparation, bursts are more prominent during natural scene viewing than while

the animal views a white noise stimuli (Lesica and Stanley, 2004). Bursts are also a more

reliable indication of visual stimuli than tonic spikes in the sense that they will more often occur

at a temporarily precise time following multiple presentations of the same stimuli (Alitto et al.,

2005). Recent work using intracellular in vivo recordings from cat dLGN have confirmed that

bursts can result from natural scene stimuli (Wang et al., 2007).

What effect do bursts have on TC transmission? This question has been investigated

using the somatosensory system of awake rabbits (Swadlow and Gusev, 2001). The first spike in

a burst recorded from the ventrobasal nucleus of the thalamus was found to be more effective

than isolated spikes or “pseudobursts” at driving presumptive cortical interneurons in barrel

cortex (Swadlow and Gusev, 2001). Pseudobursts consist of two or more spikes with ISIs less

29

than 4 ms, but are not preceded by at least 100 ms of silence. Subsequent burst spikes also show

an increased efficacy of transmission, and can lead to cortical spiking (Swadlow and Gusev,

2001). These results confirm what is known about TC transmission as described above. The

long period of quiescence preceding the bursts removes the tonic depression at the TC synapse,

increasing the efficacy of the initial burst spike. Additionally, subsequent burst spikes arrive

after short ISIs, which would also suggest an increased ability to drive cortex.

The existence of bursts has been confirmed in the mouse (Grubb and Thompson, 2005).

The properties of bursts recorded from the anesthetized mouse dLGN are very similar to those

reported in other species. In addition to having similar response properties, the percentage of

spikes in bursts in the mouse (~20%) was approximately the same as reported in other species.

Burst spiking increased in the presence of visual stimuli, and the bursts contained reliable

information about the input (Grubb and Thompson, 2005).

Ocular Dominance Plasticity

The studies described in this thesis examine the patterns of dLGN input during periods of

visual deprivation. In addition to understanding the activity entering and exiting the dLGN, it is

important to understand what is known about the experience-dependent plasticity resulting from

changes in visual experience. Beginning over 40 years ago with the pioneering studies of Hubel

and Wiesel (1963a; 1963b) ocular dominance (OD) plasticity, or the brain’s ability to change the

relative responsiveness to input from the two eyes, has been a well studied model of cortical

plasticity. While the literature surrounding this topic is vast, I will focus on the aspects specific

to the aims of this study: how various inputs affect OD plasticity; mouse OD plasticity;

differences between juvenile and adult plasticity in the mouse; and the effects of deprivation on

retinal and dLGN activity.

30

Wiesel and Hubel (1963a) first studied the effects of visual deprivation in V1 by

recording extracellularly from numerous neurons in kittens with different histories of visual

experience. They classified each neuron’s response based on the relative responsiveness to

contralateral- and ipsilateral-eye inputs (OD scores). In the normally reared kitten, the

distribution of OD scores showed a bias for neurons responding equally to inputs from both eyes,

with a slight overall bias for contralateral input. Monocular lid closure (MC) or monocular

occlusion (MO) with a translucent lens prior to eye-opening led to a loss of responsiveness to the

deprived eye, where the distribution of responses shifted entirely to the non-deprived eye.

Kittens that were allowed normal visual experience until 9 weeks of age followed by MC for 4

months showed a similar OD shift.

Subsequent studies of OD plasticity by other groups found that the OD shift can occur in

less than one day (Mioche and Singer, 1989). Mioche and Singer used chronically implanted

electrodes to follow the response properties of cortical neurons after brief periods of MO with an

eye patch in kittens. A decrease in response to the deprived eye was observed as early as 6 hr

following MO, with a complete loss of the deprived-eye response occurring as early as 12 hours

after deprivation.

Wiesel and Hubel (1965) also compared the effects of binocular lid closure (BC) with

MC. Somewhat surprisingly, they found that BC had a minimal effect on the distribution of OD

scores. From this work they concluded that the amount of plasticity observed in cortex following

suture of one eye depends on whether or not the other eye is sutured. Subsequently, several

studies have used different visual inputs to induce OD plasticity, in an attempt to understand

which input patterns result in cortical plasticity (e.g. Blakemore, 1976; Kratz and Spear, 1976;

Singer et al., 1977; Wilson et al., 1977; Chapman et al., 1986; Greuel et al., 1987).

31

Two studies that were specifically designed to elucidate the role of retinal activity in OD

plasticity compared MC with monocular retinal inactivation (MI) via intraocular injection of

tetrodotoxin (TTX), creating an imbalance in amount of input entering the visual systems

(Chapman et al., 1986; Greuel et al., 1987). Both studies showed that MI shifted responses

towards the non-deprived eye. Additionally, both research groups also recorded from animals

that had one retina inactivated while the other lid was sutured. These studies produced

contrasting results. Chapman et al. (1986) observed an OD shift towards the closed eye (which

has residual retinal activity), and Greuel et al. (1987) observed no OD shift. Therefore these

conflicting results, made it difficult to make conclusions regarding the role of retinal activity in

OD plasticity.

To resolve this conflict, Rittenhouse et al. (1999) directly compared the effects of MC

and MI in the same study. They found that brief periods (2 days) of MI caused only a minimal

OD shift (contrary to the prior studies which used extended periods of MI > 6 days), with MC

leading to a much more extreme shift towards the non-deprived eye. From these results they

concluded that the residual retinal activity that remains during MC, and is relayed from the retina

to V1 via the dLGN, is required to induce a significant OD shift. This residual presynaptic

activity, when paired with weak postsynaptic activation, leads to homosynaptic long term

depression (LTD) and a weakening of the synapses carrying deprived-eye input (Blais et al.,

1999; Rittenhouse et al., 1999). On the other hand, MI, the complete absence of retinal input,

leads to no depression. This was assumed to be due to a lack of input from dLGN to V1. With

no residual input entering cortex, there is no activity to drive the homosynaptic depression.

Subsequent studies have confirmed the connection between deprived-eye depression

following MC and the in vitro model of plasticity, LTD. The molecular and functional changes

32

that result from brief MC, including altered AMPAR phosphorylation and decreased surface

expression, are the same changes as seen following induction of LTD. Furthermore, response

depression from MC occludes the subsequent induction of LTD ex vivo (Heynen et al., 2003).

Additionally, blocking AMPAR endocytosis prevents OD plasticity (Yoon et al., 2008).

Moreover, these AMPAR changes are not found following MI. This implies that MC induces

LTD in the visual cortex, while MI does not.

Ocular Dominance Plasticity in the Mouse

Mice serve as the animal model for the research described in this thesis. While the

preliminary research was performed in cats, the important findings including the effects of MC

and MI have been replicated in the mouse. In recent years it has become clear that plasticity of

binocular connections in visual cortex is highly conserved across extant mammalian species

(reviewed in Horton and Adams, 2005). Furthermore, rodents are phylogenetically closer to

primates than carnivores are, thus they may serve as a better model for understanding the human

brain (Arnason et al., 2002). Even though rodents do lack ocular dominance columns observed in

carnivores and old-world primates, they exhibit robust OD plasticity following MC (described

below).

Among the advantages of using mice as a model system are: that they have been in-bred

to minimize inter-individual variability, they are plentiful and relatively inexpensive, and they

are amenable to genetic manipulations. Furthermore, we can easily perform both acute and

chronic electrophysiological studies from the awake mouse preparation. All these features allow

more rapid progress than in many other species. Plasticity in rodent visual cortex can also be

compared to the somatosensory “barrel” cortex to discern features of cortical plasticity that may

generalize across areas. Additionally, because the dLGN of the mouse is relatively homogenous,

33

lacking multiple relay cell types (Sherman and Guillery, 2006), the findings may be more easily

extrapolated to the other primary thalamic nuclei.

Originally studied by Drager (1978), OD plasticity occurs in the mouse and is similar to

the plasticity observed in other species. In the normally reared mouse, the distribution of OD

scores shows a bias towards the contralateral eye. Converting the distribution of OD scores to a

relative response ratio, she found mice have a contralateral-to-ipsilateral (C/I) ratio of about 2 to

1. In animals that have undergone MC from eye-opening, there is an OD shift towards the non-

deprived eye. While the shift in the hemisphere contralateral to the closed eye is not as extreme

as the shifts observed in cat, the majority of the neurons have responses dominated by the non-

deprived eye. In the hemisphere ipsilateral to the deprived eye, nearly all neurons became

monocular, responding only to the non-deprived eye.

Frenkel and Bear (2004) used chronic field potential recording electrodes to follow

effects of MC and BC in awake, juvenile mice. Similar to Drager’s (1978) finding with single

unit recordings, the normal mouse shows a 2 to 1 ratio of visual evoked potentials (VEPs)

recorded in response to input from the contralateral and ipsilateral eye. MC, but not BC, leads

to a statistically significant change in the C/I VEP ratio, corresponding to an OD shift away from

the deprived eye. One advantage to the chronic recording method is the ability to separate the

changes in the responses of the two eyes. Two distinct responses in V1 lead to the change in the

C/I ratio. First there is a rapid depression of the deprived-eye response, followed by a delayed

potentiation of the non-deprived eye response. The deprived-eye depression peaks after 3 days

of MC, while the open-eye potentiation becomes significant after 7 days of MC (Frenkel and

Bear, 2004).

34

Frenkel and Bear (2004) also examined the effects of MI on VEPs in V1. Unlike the

deprived-eye depression observed following MC, there was no change in the deprived-eye

response following MI. However, there was a significant potentiation of ipsilateral eye response

in the same hemisphere. This implies that any OD shift observed in cats following MI resulted

not from a loss of response to the silenced retina, but rather an increase in the response of the

other eye. MI appears to protect against the initial stage of the OD shift (the reduction in the

deprived-eye response), while still allowing the open-eye potentiation.

The Concept of a “Critical Period” for Ocular Dominance Plasticity

In their original study of OD plasticity, Wiesel and Hubel (1963a) recorded from one cat

that underwent 3 months of MC in adulthood. There was no significant shift in the distribution

of OD scores for this animal. A careful investigation of the ages in which the cat is most

susceptible to the effects of MC showed that kittens begin to a show an OD shift after 4 weeks of

age, with a peak shift occurring following MC when the animal is 6 – 8 weeks of age (Hubel and

Wiesel, 1970). After this peak, the sensitivity to MC declines, with no shift observed after 3

months of age. Furthermore, after experiencing MC during this period of susceptibility, normal

visual experience in adulthood will not correct the OD shift, rendering the animal amblyopic

(Hubel and Wiesel, 1970).

Rodents have also been shown to have a “critical period” for OD plasticity via MC (e.g.

(Fagiolini et al., 1994; Gordon and Stryker, 1996). Early work in the rat showed that the critical

period begins at around 3 weeks of age and tapers off after 5 weeks (Fagiolini et al., 1994). Four

days of MC in the mouse caused the most robust loss of deprived-eye response at postnatal day

(P)28, with no shift occurring after P32 (Gordon and Stryker, 1996). However, more recent

35

work in rodents has shown that OD plasticity persists in the mature brain (Hofer et al., 2006a;

Iny et al., 2006; Hooks and Chen, 2007).

Several studies in the rat have shown that the critical period in the rat can be

“reactivated.” Cortical infusion of drugs that break down the perineuronal nets in the

extracellular matrix which may prevent anatomical changes from occurring provide one way to

reopen the critical period in adults (Pizzorusso et al., 2002; Pizzorusso et al., 2006). Non-

invasive options including environmental enrichment (Sale et al., 2007) and periods of dark

exposure (He et al., 2007) have also been shown to allow for juvenile-like OD plasticity in

adults.

Recent studies suggest that the classically defined critical period for OD plasticity may

not even exist in the mouse (reviewed in Hofer et al., 2006a). Changes in the expression of the

immediate early gene, Arc, show an expansion of the non-deprived eye input to V1 following

MC well past the traditional end of the critical periods (13 weeks) (Tagawa et al., 2005).

Intrinsic signal optical imaging and extracellular single-unit recording techniques also verified

OD plasticity in the adult mouse, although this OD shift was attributed to a potentiation of the

non-deprived eye input, with no depression of the deprived-eye response (Hofer et al., 2006b).

Furthermore, VEP recordings in the adult mouse show that extended periods of MC (7 days) lead

to both deprived-eye depression and open-eye potentiation, while shorter periods of MC (3 days)

are not accompanied by a loss of the deprived-eye response (Frenkel et al., 2006). While these

findings suggest that the concept of a “critical period” may not exist in the mouse, there is a

qualitative difference between juvenile and adult OD plasticity in this species.

36

Spontaneous Retinal Ganglion Cell Activity

While much is known about the effects of monocular deprivation in V1, fewer studies

have examined the physiological consequences of visual manipulation in the retina and dLGN.

Closing the eyelid leaves spontaneous RGC activity as the driving input to the dLGN. While one

may assume that this activity is purely random, RGC activity does show correlations, even in the

absence of visual input (reviewed in Mastronarde, 1989). Single-unit recordings from the retina

of cats have shown that neighboring RGCs can be highly correlated (Rodieck, 1967). This

correlation depends on the sign of the RGCs (ON- or OFF-center), with neurons of the same sign

showing positive correlation, and neurons with opposite sign showing negative correlations.

Furthermore, the degree of overlap of the RF centers determines the magnitude of the correlation

(Mastronarde, 1989). Shared input from rods or from spiking amacrine neurons, or gap junction

connections between RGCs may account for the correlated firing (Mastronarde, 1989).

Effects of Monocular Deprivation on dLGN Activity

Monocular lid suture only has small effects on the response properties of neurons in the

dLGN. The original study by Wiesel and Hubel (1963b) showed the effects of lid suture from

birth until 3 months of age in kittens. The physiological effects of the lid suture were minimal,

with neurons in the deprived dLGN layers retaining their RF size and shape. Some neurons

showed an increase in response latency, and the authors report a slight decrease of overall

activity in the deprived layer. Anatomically, some atrophy was observed in the layer receiving

deprived-eye input (Wiesel and Hubel, 1963b).

Subsequent studies in the cat show similar findings to those reported by Wiesel and

Hubel (Sherman and Sanderson, 1972; LeVay and Ferster, 1977; Lehmkuhle et al., 1980;

Friedlander et al., 1982). In a study where X- and Y-cells were evaluated separately, there was

37

no physiological effect on the Y cells and a small decrease in contrast sensitivity in the X-cells

(Lehmkuhle et al., 1980). However, there does appear to be a loss of Y-, but not X-, cells in the

deprived laminae (LeVay and Ferster, 1977), corroborating the atrophy observed by Wiesel and

Hubel. Further studies by Friedlander et al. (1982) confirm the morphological shrinkage of the

Y-cells, and find physiological deficits in the remaining Y-cells, probably overlooked in the prior

studies due to difficulties in recording from the smaller cell bodies.

Extended periods of MC in primates also result in small physiological changes. Four

years of monocular lid suture in the galago (Sesma et al., 1984) and five to six years of

monocular lid suture in macaque monkeys (Levitt et al., 2001) led to only subtle changes in

dLGN response properties, however atrophy was observed in both parvo- and magnocellular

cells. One explanation for atrophy being observed in both cell types in the primates but not the

cats is that the parvo- and magnocellular cells are separated into different layers in the primates,

while X- and Y-cell types are intermingled within layers in the cat. This may lead to competition

for retinal input between X- and Y-cells, which seems to result in selective Y-cell atrophy

(Sesma et al., 1984).

Previous Recordings from dLGN Neurons During Deprivation

There are very few studies of the effect of deprivation on dLGN activity during periods

of deprivation. In fact, there are only two reports describing dLGN activity during MI in the

anesthetized cat (Stryker and Harris, 1986; Rittenhouse et al., 1999). In the Stryker and Harris

(1986) study, recordings were made 88 - 100 hours after injection of TTX. At this time point,

the pupil of the injected eye was no longer dilated and the consensual pupilary response was

observed when light was applied to the non-injected eye. Recordings were made ipsilateral to

the injected eye, in the A1 lamina of the dLGN. While recordings from the A and C layers were

38

normal, the deprived layer showed units that were silent or had very low levels of spontaneous

firing, with no evidence of evoked responses. Visual responses through the previously deprived

eye were observed in cortex 10 – 22 hours later (Stryker and Harris, 1986). These results

suggest that the dLGN neurons showed a large reduction in activity during MI. However,

recordings performed at an earlier time-point are necessary to confirm that assumption.

In the study by Rittenhouse et al. (1999) dLGN recordings were made within 48 hours of

TTX injection. They also observed a huge decrease in spontaneous dLGN activity (~80%),

confirming the results of Stryker and Harris (1986). Taken together, these two studies suggest

that the amount of dLGN activity during retinal inactivation is greatly reduced in the

anesthetized cat preparation.

Rittenhouse et al. (1999) also recorded from the dLGN during MC. The spontaneous

firing rate in the dLGN was higher than during MI, although they did not compare MC to an

open eye response rate. They conclude that the important difference between MC and MI is the

amount of spontaneous firing in the dLGN (Rittenhouse et al., 1999). Work from Kaplan et al.

(1987) shows that the dLGN firing rate is lower than the retinal firing rate during both

spontaneous and stimulus-driven conditions. While they do not specifically examine MC and

MI, this would suggest that the dLGN firing rate is highest during open-eye viewing, lower

during MC when only spontaneous retinal activity remains, and near zero during MI. However,

one caveat to all of these studies is that they were performed in the anesthetized cat. To fully

determine the dLGN firing responses during open-eye viewing, MC, and MI, careful studies

need to be performed in the awake animal. The goal of this project is to explore dLGN activity

under these 3 viewing conditions in the awake animal, in order to understand the contribution of

presynaptic input to OD plasticity.

39

Figure 1-1

Figure 1-1 “Hidden lamination” in the mouse dLGN

The mouse dLGN consists of an outer shell receiving contralateral eye input and an inner core

receiving ipsilateral eye input. a, Confocal image of a coronal section from a mouse brain

following intraocular injections of cholera toxin B (CTB). CTB is an anterograde tracer that fills

neuronal axons. CTB conjugated to Alexa 555 was injected in the ipsilateral eye (indicated in

red), and CTB conjugated to Alexa 647 was injected into the contralateral eye (indicated in

blue). The mouse was a transgenic animal expressing GFP under the GAD67 promoter

(Chattopadhyaya et al., 2004), thus inhibitory interneurons are labeled in green. Scale bar: 200

μm. b, Schematic depicting the contralateral shell and ipsilateral core of the dLGN. Both of

these areas project to the binocular zone of V1.

41

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Summary

The dLGN was classically thought to be a mere relay of visual information from the

retina to the primary visual cortex. However, the anatomical and physiological features of this

nucleus suggest that the signal is transformed from the eye to V1. This transformed signal serves

as the presynaptic input to V1 that drives OD plasticity under MC and MI conditions. While

assumptions regarding the nature of dLGN activity are made based on the nature of the visual

manipulation, no careful study of dLGN activity during MC and MI has been conducted. The

purpose of this thesis research is to examine dLGN activity during MC, MI and normal viewing

conditions to capture the activity that leads to deprived-eye depression. Additionally, we

compare the findings from juvenile and adult mice in an attempt to explain the differences in OD

plasticity as the animal matures.

Chapter 2

LGN ACTIVITY DURING OCULAR DOMINANCE

PLASTICITY IN THE JUVENILE MOUSE

Adapted from

Linden, M.L., Heynen, A.J., Haslinger, R.H., Bear, M.F (2008) Thalamic activity patterns during

ocular dominance plasticity. Submitted.

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Abstract

Ocular dominance (OD) plasticity is a classic example of experience-dependent cortical

plasticity that results from a brief period of monocular lid closure (MC) during early postnatal

development (Wiesel and Hubel, 1963). The initial phase of the OD shift is characterized by a

decreased cortical response to input from the deprived eye (Mioche and Singer, 1989; Frenkel

and Bear, 2004). While this deprived-eye response depression was originally thought to be

caused by the general reduction of cortical input during MC, monocular retinal inactivation (MI),

which eliminates all retinal activity, does not yield this depression (Rittenhouse et al., 1999;

Frenkel and Bear, 2004). It has therefore been suggested that residual activity in the deprived

retina drives MC-induced cortical depression. This implies that dorsal lateral geniculate nucleus

(dLGN) activity during MC leads to cortical plasticity that subsequently produces changes in

visual function, while dLGN activity during MI does not. However, the actual patterns of dLGN

neural activity resulting in OD plasticity have yet to be characterized. Here we describe neural

activity patterns recorded extracellularly from dLGN of awake mice before and after MC and

MI. We had hypothesized that MC would produce uncorrelated dLGN activity (“noise”)

resulting from spontaneous retinal activity in the absence of visual input, whereas MI would be

accompanied by a marked reduction in dLGN activity. We have verified that dLGN activity

following MC is distinguished by a lack of patterned unit activity. During MI however, instead

of a reduction of neural activity, we unexpectedly found highly patterned activity which is

characterized by thalamic bursting and an increase in simultaneous firing. These findings

provide the first description of dLGN unit activity in the awake mouse that is known to lead to a

loss of visual function. Furthermore, we provide the first evidence that MI does not reduce

54

thalamic input to visual cortex. These findings have important implications for our

understanding of the mechanisms supporting experience-dependent cortical plasticity.

Introduction

Experience-dependent cortical plasticity is a well studied phenomenon of the mammalian

brain. While much is known about the biochemical, electrophysiological and anatomical

changes that occur in the cortex, far less is known about the input activity to cortex that drives

cortical plasticity in vivo. OD plasticity is a particularly well characterized example of

experience-dependent cortical plasticity. Brief periods of MC in young kittens shift OD in

primary visual cortex (V1) (Wiesel and Hubel, 1963), primarily due to a loss of visual

responsiveness to the deprived eye (Mioche and Singer, 1989). Although eyelid closure causes

deprived-eye response depression, completely blocking all input from the eye in kittens by MI

(via intraocular injection of tetrodotoxin, TTX) does not lead to a robust OD shift (Rittenhouse et

al., 1999). Therefore, it has been concluded that OD plasticity requires a basal level of retinal

activity. These findings have recently been replicated using local field potential recordings in

V1 of awake juvenile mice (Frenkel and Bear, 2004). These studies confirm that the OD shift

following MC initially results from depression of the deprived-eye response and that this

depression does not occur following MI.

Since visual input travels from the retina to the dLGN and then into V1, we can conclude

that the patterns or amount of dLGN activity during MC trigger deprived-eye response

depression in vivo, while the patterns and amount of dLGN activity during normal vision (NV)

and MI do not. Although these activity patterns are crucial for understanding how OD plasticity

is initiated in visual cortex, they are essentially unknown. It has been inferred from a limited

number of studies that dLGN activity during deprivation approximates that in the retina (Stryker

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and Harris, 1986; Kaplan et al., 1987; Rittenhouse et al., 1999). Thus, it has been assumed that

(1) during NV, images formed on the retina translate into spatially and temporally patterned

dLGN activity, (2) patterned activity is lost during MC, leaving only a low level of residual

spontaneous activity, and (3) the absence of retinal activity during MI substantially reduces or

eliminates activity in the dLGN. However, the experiments supporting these assumptions have

all been performed in the paralyzed and anesthetized preparation that does not support OD

plasticity (Freeman and Bonds, 1979). Therefore, the goal of the current study was to understand

how dLGN activity varies under the conditions that trigger rapid shifts in OD in vivo. We chose

to examine this question in the mouse because this species is amenable to awake recordings and

is now preferred for mechanistic studies.

Methods Summary

On postnatal day (P) 25, male mice were anesthetized and surgically prepared for head

restraint and electrophysiological recordings (see Detailed Methods). The animals were

behaviorally habituated to restraint over the next several days, and bundles of electrodes were

inserted in the dLGN at the age of maximal sensitivity to MC (~P28) (Gordon and Stryker,

1996). The location of the recording bundle in the dLGN was initially determined by

observation of visually-driven unit activity in response to stimulation of the contralateral eye,

and was always subsequently confirmed histologically (Fig. 2-1). Baseline recordings were

made with the eye contralateral to the electrode viewing and the ipsilateral eye occluded.

Activity approximating that during NVE was recorded in response to phase-reversing sinusoidal

gratings and natural scene stimuli (see Methods). Unless otherwise indicated, results using

grating stimuli are only illustrated since results from natural scenes did not differ qualitatively.

Following the baseline recording session, animals were briefly anesthetized and either eyelid

56

closure (MC), TTX injection (MI), or no manipulation (control), was performed. After 30

minutes of recovery from anesthesia, stimuli were presented for a second recording session. For

the MI group, effectiveness of TTX was assessed by observing pupilary dilation (Fig. 2-1b) both

before and after the second recording session. At the conclusion of recordings, an electrolytic

lesion was made at the recording site to allow for histological confirmation of electrode

placement (Fig. 2-1d).

Results

Figure 2-2 shows the responses of one representative neuron from each experimental

group. In the baseline condition, each neuron shows a visual response to the grating stimulus as

indicated by an increase in the number spikes immediately following the phase reversal. As

expected, the neuron from the control group shows a qualitatively similar visually-driven

response during both recording sessions (Fig. 2-2a). The neuron from the MC group (Fig. 2-2b)

is no longer visually driven after eyelid closure, but continues to show a high level of

spontaneous activity. Most surprising however, is the activity of the neuron from the MI group

(Fig. 2-2c) which does not show an absence or even a reduction of neural activity following the

TTX injection. Rather, this neuron shows a high level of spontaneous activity after retinal

inactivation, similar to what is seen from the MC neuron.

To quantify these observations, we measured the overall firing rate during the entire

recording session before and after eye manipulation (Fig. 2-3). We found no significant change

in firing rate across any of the experimental groups (p > 0.5, Kruskal-Wallis). Since

measurements of overall firing rate can be considered crude measures of neural activity, as they

do not take into account changes in the temporal pattern of the neural spike train, we next

examined the distribution of inter-spike intervals (ISIs) before and after eye manipulation.

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Figure 2-4a,b show that there is no change in the ISI distribution in the control or the MC groups

(control: p > 0.2, MC: p > 0.05, Kolmogorov-Smirnov Bootstrap (KS)). In contrast, the MI

group shows a leftward shift in the ISI distribution (Fig. 2-4c), indicating an increased

probability of observing very short ISIs (p < 10-9

, KS). Indeed this corresponds to an increased

probability of observing ISIs from 2 – 4 ms (Fig. 2-4d).

Activity exhibiting very short ISIs is reminiscent of the phenomenon of bursting, which is

common in thalamic relay cells (Sherman, 2001). The criteria for bursts consist of an initial

period of quiescence (> 100 ms) followed by two or more spikes with ISIs < 4 ms (Lu et al.,

1992) (Fig. 2-5a). While originally thought to occur only during sleep states, bursts have now

been shown to occur in awake animals including cats (Guido and Weyand, 1995) and primates

(Ramcharan et al., 2000). Bursts in the dLGN have been related to visual processing, as the

percentage of spikes in bursts increases during natural scene viewing as compared to white-noise

viewing (Lesica and Stanley, 2004). However, bursting has not previously been documented in

the awake mouse preparation.

Since we observed an increase in very short ISIs, we next quantified bursting activity.

Figure 2-5b shows the occurrence of bursts on raster plots from one representative neuron in

each of the three experimental conditions. We observed a small, comparable percentage of burst

spikes in the control and MC conditions, with some of the control bursts apparently related to

visual stimulus presentation. However, as suggested by the shift in ISIs, we found a large

increase in the percentage of spikes in bursts after retinal inactivation. Group analysis (Figure 2-

5c) demonstrated no change in overall bursting in the control and MC groups (p > 0.7 and p >

0.2 respectively, Wilcoxon Sign-Rank (WSR)) but a large increase in the percentage of spikes in

bursts following MI (p < 10-3

, WSR).

58

Because we utilized an acute recording paradigm, it is possible that the observed bursting

was only a transient feature of dLGN firing activity. To address this concern, we chronically

implanted the recording bundle and sampled unit activity throughout the period of retinal

inactivation. Bursting activity persisted throughout the period of MI, up to at least 48 hours (Fig.

2-6). Additionally, the burst percentage returned to baseline values after the TTX washed out, as

confirmed by pupil constriction and a return of visual response (Fig. 2-6d,e).

Our observation of an increase in burst spikes during MI was entirely unexpected.

Although bursting following retinal inactivation or eye enucleation has been described in the

immature ferret dLGN prior to natural eye-opening (Weliky and Katz, 1999), it has long been

assumed that by adolescence, monocular TTX treatment simply reduces activity in the central

visual system. Moreover, it has been suggested that bursts carry specific information about

patterned visual input in mature animals (Lesica and Stanley, 2004). Thus, it was equally

surprising to find that there was no significant decrease in bursts following MC compared to the

NV condition in the adolescent mice.

Occasionally we recorded from the ipsilateral core of the dLGN instead of our intended

target of the contralateral shell. Remarkably, even though the retinal input to this brain area had

not been changed, we found robust changes in the firing properties of these neurons including an

increase in firing rate (p < 0.02, WSR) and an increase in burst percentage (p < 0.01, WSR) (Fig.

2-7).

None of the properties of individual spike trains that we examined differentiated NVE

and MC. Therefore, we next looked to the correlative firing patterns between simultaneously

recorded neurons for an explanation of what triggers OD plasticity. Figure 2-8a shows example

cross-correlograms representing pairs of simultaneously recorded neurons from each of the three

59

experimental groups. Scatter plots showing the amount of correlative firing before and after

visual manipulation are shown in figure 2-8b, with the data summarized in figure 2-8c. Unlike

the control and MI groups, there was a decrease in the simultaneous firing between dLGN

neurons for nearly every pair of neurons following MC (p < 10-3

, WSR).

In the MI group, however, we observed an increase in simultaneous firing (p < 0.01,

WSR). To determine if this increase was a direct result of the increase in burst spikes, we

analyzed the correlative firing of just the initial spikes in the bursts, as well as the correlations

with all burst spikes removed (Fig. 2-8d). The initial bursts spikes were significantly correlated

(p < 0.03) while the non-burst spikes were not (p > 0.9). This implies that the increased in

bursting accounts for the increase in simultaneous firing following MI.

Discussion

MC and MI have very different effects of dLGN firing activity. While neither

manipulation causes a decrease in the amount of firing, MC results in a decrease of correlative

firing between pairs of simultaneously recorded neurons, and MI causes an increase in thalamic

bursting. These results provide insights into the mechanisms which drive OD plasticity.

It has been known for decades that the change in dLGN activity during MC causes rapid

depression of deprived-eye responses in the visual cortex. Theoretically, this could occur as a

consequence of either a decrease or a de-correlation of deprived-eye input to cortex relative to

the seeing eye (Blais et al., 1999). This distinction is important for understanding cortical

plasticity. The former explanation requires a mechanism whereby active inputs “punish”

inactive inputs (heterosynaptic depression); the latter explanation requires a mechanism by

which the activity of poorly correlated inputs triggers their own demise (homosynaptic

depression). Our data reveal no difference in the amount or temporal structure of dLGN activity

60

following eyelid closure. Instead, our findings support the “homosynaptic” hypothesis that

poorly correlated dLGN activity is the trigger for deprived-eye depression in visual cortex. This

supports the recent finding that monocular blur is as effective as MC in driving OD plasticity

(Rittenhouse et al., 2006).

The synaptic depression observed following MC has now been shown to employ

mechanisms of homosynaptic long-term depression (LTD). Surface AMPA receptor (AMPAR)

expression decreases following both brief periods of MC and the induction of LTD (Heynen et

al., 2003). Blocking AMPAR endocytosis in vivo prevents OD plasticity (Yoon et al., 2008).

Also, MC occludes subsequent expression of LTD ex vivo (Heynen et al., 2003). In contrast, MI,

which does not lead to deprived-eye depression, does not engage LTD-like mechanisms (Heynen

et al., 2003). Therefore, we can conclude that patterns of dLGN activity during MC lead to OD

plasticity and LTD in vivo, while patterns of dLGN activity during MI do not. Having

characterized the activity patterns which drive synaptic depression may lead to the development

of novel LTD induction protocols which will allow us to better model cortical plasticity in vitro.

One methodological concern related to the observation of a decrease in correlative firing

stems from the choice of visual stimuli. Because the animals viewed a full-screen sinusoidal

grating, there may have been an artificial increase in simultaneous firing during baseline

viewing, as neurons with non-overlapping receptive fields may be activated together. To address

this concern, the animals were also shown “natural scene” images, in the hopes of better

approximating the types of visual input the animal would be exposed to during a period of MC

that leads to an OD shift. The maintenance of overall dLGN firing activity under all viewing

conditions, the increase in burst spikes during MI, and the decrease in simultaneous firing during

MC all hold when the animal is viewing the natural scene stimuli (Fig. 2-9). This suggests that

61

deprived-eye depression due to homosynaptic mechanisms is in fact what occurs in V1 during

standard OD plasticity protocols.

Our data challenge the interpretation of previous experiments that used MI as a means of

distinguishing between homosynaptic and heterosynaptic hypotheses. The fact that MI induces

substantially less deprived-eye depression than MC (Rittenhouse et al., 1999; Heynen et al.,

2003; Frenkel and Bear, 2004) was taken as evidence that dLGN activity is necessary to trigger

synaptic modifications in visual cortex. Instead of reduced dLGN activity after MI, however, we

find that dLGN neurons are as active and correlated as during normal visual experience, and fire

substantially more spikes in bursts. This finding contrasts with previous reports that MI

decreases dLGN firing rate in the anesthetized kitten (Stryker and Harris, 1986; Rittenhouse et

al., 1999). To determine if this discrepancy is due to the use of anesthesia, we performed MI in

anesthetized mice. Indeed, compared to baseline, a significant decrease in firing rate is observed

following MI in this preparation (Fig. 2-10a,b). Furthermore, we see a substantial increase in the

amount of burst spikes during the baseline condition under anesthesia (Fig. 2-10c). This may

explain the discrepancy between the results from anesthetize cat recordings, where bursts are

more prevalent during natural scene viewing as opposed to white noise (Lesica and Stanley,

2004), and our results where lid closure (presumably resulting in a more “noisy” retinal

response) had no effect on burst percentage. These analyses underscore the importance of using

an awake preparation when determining the patterns of activity which drive cortical plasticity.

Certain behavioral states are correlated with an increase in burst percentage (e.g.

Bezdudnaya et al., 2006). Because we saw an increase in burst percentage following MI, it is

necessary to confirm that this increase was not due to a change in behavioral state. To address

this concern, we analyzed the cortical EEG recorded from a subset of mice in the control and MI

62

groups. Power spectral analysis of EEG recorded from occipital cortex was performed in 10 s

bins using the Welch method of spectral estimation. We defined the delta band as frequencies

below 4 Hz and the theta band as frequencies between 4.5 and 9 Hz. We used the theta /delta

ratio (TDR) as our measure of behavioral state (Maloney et al., 1997). We found no change in

behavioral state following MI, nor did we find a correlation between TDR and the number of

observed bursts (See Appendix 2).

How can we account for the remarkable activity in the dLGN postsynaptic to the inactive

retina? The bursting activity in the dLGN after MI resembles that observed during sleep in this

brain region. Such bursts result from the activation of T-type Ca2+

channels, which require a

long period of hyperpolarization for deinactivation (Sherman, 2001). In the absence of retinal

input, these channels may have more opportunity for deinactivation. Since retinal inputs

comprise such a small percentage of the total input into the dLGN (Sherman and Guillery, 1998),

the input from the other sources, including feedback input from V1, could depolarize dLGN

neurons above the activation potential for Ca2+

channels, which would in turn lead to burst

spikes.

Our results provide a surprising alternate explanation for the lack of deprived-eye

depression following MI. Instead of an absence of input preventing depression, the active,

patterned firing of dLGN neurons may protect against deprived-eye depression by substituting

for normal visual experience. Furthermore, prolonged MI leads to potentiation of the ipsilateral

response (Frenkel and Bear, 2004). The bursting of the ipsilateral core may be the activity that

underlies the potentiation of that connection. While this change in firing properties is surprising

given the lack of change in the retinal input, local dLGN circuitry, intrathalamic circuitry via the

thalamic reticular nucleus, or corticothalamic feedback may be responsible for the change.

63

The dramatically different consequences on visual cortex of deprivation by eyelid closure

or image blur (Rittenhouse et al., 2006) and retinal inactivation (Freeman and Bonds, 1979;

Rittenhouse et al., 1999; Heynen et al., 2003; Frenkel and Bear, 2004) are accounted for by

equally dramatic differences in dLGN activity. Characterization of the activity patterns which

drive homosynaptic depression will allow us to better model and more precisely determine the

mechanism of cortical plasticity. This new knowledge may aid in identifying new treatment

options for amblyopia.

65

Figure 2-1

Figure 2-1 Methodology.

a, Experimental setup. Data were obtained from awake, head-restrained mice viewing grating

stimuli. b, Intraocular delivery of tetrodotoxin (TTX) was confirmed by pupilary dilation. Left

column: non-injected eye, right column: TTX-injected eye. Lower row: circles outline the pupil.

c, Schematic of recording electrode placement in the dLGN. d, Histological confirmation of

recording electrode track in dorsal LGN. Arrow indicates site of electrolytic lesion (10 µA; 10 s)

made at the end of recording session.

67

Figure 2-2

Figure 2-2 dLGN responses before and after visual manipulation.

a – c, Peristimulus time histograms and raster plots from representative neurons for each

experimental group (80 stimulus trials) are presented. Stimuli were presented at 0° or 90°, 1 Hz

phase reversing (▼). Arrowheads in this and subsequent figures indicate time of stimulus phase

reversal. Spike waveforms are recording session averages. Scale bar: 100 µV, 500 µs. Left

column data were obtained during baseline, right column after eye manipulation. Neuron (a) is

from the control group, (b) MC group, and (c) MI group. Note the loss of visual responsiveness

with MC and MI, and that the total number of spikes recorded is comparable before and after

each manipulation.

69

Figure 2-3

Figure 2-3 Firing rate before and after visual manipulation.

Monocular lid closure and inactivation have no effect on dLGN firing rate (p > 0.5, Kruskal-

Wallis (KW)). Firing rate was calculated for the full duration of visual stimulation across all

stimulus conditions. Connected gray circles represent the same neuron recorded before and after

eye manipulation ((O)pen, (C)losed, (I)nactived). Black lines indicate median values (control: n

= 22 neurons (9 animals), p > 0.2 Wilcoxon Sign-Rank (WSR); MC: n = 24 neurons (12

animals), p > 0.3; MI: n = 19 neurons (8 animals), p > 0.3). See also Fig. 2-9a.

71

Figure 2-4

Figure 2-4 Interspike interval distributions before and after visual manipulation.

ISI distributions are not affected in control (a) and MC groups (b). Plots represent mean

cumulative ISI distribution during baseline (black), and after eye manipulation (gray) (control: p

> 0.1 Kolmogorov-Smirnov Bootstrap (KS); MC: p > 0.08). c – d, MI increases the probability

of observing short ISIs. c, cumulative probability distributions for MI as described in (a,b) (p <

10-5

). d, Probability density function; the curves differ significantly from 2 - 4 ms (p < 0.01

WSR).

73

Figure 2-5

Figure 2-5 Monocular inactivation increases the percentage of spikes in bursts.

a, Schematic of burst firing. A burst is defined by at least 100 ms of quiescence followed by two

or more spikes with ISIs < 4 ms. b, Raster plots of 80 stimulus trials from neurons nearest the

median in each experimental group. Stimuli were presented at 0° or 90°, 1 Hz phase reversing

(▼). Black squares represent spikes in bursts; gray squares non-burst spikes. Note bursts on the

leading edge of the visual response in the open eye condition and the marked increase in bursts

in the MI condition. c, MI increases the percentage of spikes in bursts (p < 10-3

KW; MI n = 19

neurons (8 animals), p < 10-3

WSR). Connected gray circles represent the same neuron recorded

before and after eye manipulation. Black lines indicate the median values (control: n = 22

neurons (9 animals), p > 0.7; MC: n = 24 neurons (12 animals), p > 0.2). See also Fig. 2-9b.

75

Figure 2-6

Figure 2-6 An increase in the percentage of spikes in bursts persists throughout an extended

period of monocular inactivation.

a – d, Raster plots of 80 stimulus trials from representative neurons prior to, during and

following MI. Stimuli were presented at 0° or 90°, 1 Hz phase reversing (▼). Black squares

represent spikes in bursts; gray squares non-burst spikes. a, baseline condition 2 hrs prior to

TTX injection; b – c, 24 and 48 hrs after TTX injection, respectively; d, recovery from MI, 120

hrs after injection. Note that the neurons are visually responsive only during the baseline and

recovery conditions (a, d), and the marked increase in bursts throughout the period of

inactivation (c – d). e, The percentage of spikes in bursts remains elevated at least 48 hrs

following TTX injection and returns to baseline values following recovery from MI (KW effect

of time, p < 0.01). Gray circles represent individual neurons. Black lines indicate the median

values. At 2, 24 and 48 hrs, the percentage of spikes in bursts is significantly different from both

the baseline and recovery (120 hrs) time points (p < 0.05 for all comparisons, Mann-Whitney U-

test (MWU)). The baseline and recovery time points are not significantly different (p > 0.7,

MWU), nor are the time points during MI (p > 0.4 for all comparisons, MWU).

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Figure 2-7

Figure 2-7 Inactivation of the contralateral eye changes the firing patterns of the dLGN

ipsilateral core.

a, Raster plots of 80 stimulus trials from neurons nearest the median in each experimental group.

Stimuli were presented at 0° or 90°, 1 Hz phase reversing (▼). Black squares represent spikes in

bursts; gray squares non-burst spikes. Note bursts on the leading edge of the visual response in

when the ipsilateral (center panel) but not contralateral (left panel) eye is viewing. Following MI

of the contralateral eye (right panel), a visual response is maintained in addition to the marked

increase in bursts and overall firing. b, Contralateral MI increases dLGN firing in the ipsilateral

core (n = 9 neurons (4 animals), p < 0.02). Connected gray circles represent the same neuron

recorded before and after eye manipulation. Black lines indicate the median values. c,

Contralateral MI increases the percentage of spikes in bursts in the ipsilateral core (n = 9 neurons

(4 animals), p < 0.01 WSR).

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Figure 2-8

Figure 2-8 Monocular lid closure and monocular inactivation have opposite effects on

correlative dLGN firing.

a, Cross-correlograms from three pairs of simultaneously recorded neurons before (black line)

and after (gray line) visual manipulation. b, Scatter plots of the area under the cross-

crosscorrelogram before and after visual manipulation for each pair of simultaneously recorded

neurons. Gray line represents unity. Note that following MC (center panel) nearly all points fall

below the unity line, indicating a decrease in correlative firing. c, MC and MI have opposite

effects on spike correlation (p < 10-4

KW). Bars represent the median change in area under the

peak of the cross-correlogram (± 10 ms) following visual manipulation. Error bars show the

interquartile range. MC and MI induce significant changes in correlation (control: n = 22 neuron

pairs (6 animals), p > 0.2 WSR; MC: n = 20 neuron pairs (6 animals), p < 10-3

; MI: n = 18

neuron pairs (6 animals), p < 0.01). See also Fig. 2-9c. d, Bursts contribute to increased

correlation following MI. Data are represented as in c. Cross-correlograms using all the data

are plotted in the “All Spikes” bar which recapitulates I from c. “Bursts” are cross-correlograms

of only the first spikes from all bursts (n = 18 neuron pairs (6 animals), p < 0.03). “Non-Bursts”

are cross-correlograms of all spikes not contained in bursts (p > 0.9).

81

Figure 2-9

Figure 2-9 The effects of monocular lid closure and monocular inactivation on dLGN activity

when assessed using natural scene stimuli are comparable to that observed using phase-reversing

sinusoidal grating stimuli.

a, Monocular lid closure (C) and inactivation (I) have no effect on dLGN firing rate (p > 0.7,

KW). Connected gray circles represent the same neuron recorded before and after eye

manipulation. Black lines indicate the median values (control: n = 22 neurons (9 animals), p >

0.3 WSR; MC: n = 24 neurons (12 animals), p > 0.8; MI: n = 19 neurons (8 animals), p > 0.5).

b, MI increases the percentage of spikes in bursts (p < 0.01 KW; control: n = 22 neurons (9

animals), p > 0.8; MC: n = 24 neurons (12 animals), p > 0.8; MI: n = 19 neurons (8 animals), p <

0.01 WSR). c, MC leads to a decrease in spike correlation (p < 10-4

, KW; MC: n = 20 neuron

pairs (6 animals), p < 10-3

WSR). Bars represent the median change in area under the cross-

correlogram following visual manipulation. Error bars show the interquartile range (control: n =

22 neuron pairs (6 animals), p > 0.9; MI: n = 18 neuron pairs (6 animals), p < 0.02).

83

Figure 2-10

Figure 2-10 Monocular inactivation decreases firing rate if the animal is under Nembutal

anesthesia.

a, Peristimulus time histograms and raster plots (80 stimulus trials) for one representative neuron

are presented. Stimuli were presented at 90°, 1 Hz phase reversing (▼). Spike waveforms are

averages over the entire recording session. Scale bar: 100 mV, 500 µs. Left panel: responses

during baseline, right panel: after MI under barbiturate anesthesia. b, Neuronal firing rate

decreases after MI. Connected gray circles represent the same neuron recorded before and after

manipulation ((O)pen, (I)nactivated). Black lines indicate the median values (n = 9 neurons (3

animals), p < 0.05 WSR). c, Percentage of spikes in bursts decreases after MI (p < 0.03). Notice

that during baseline, burst spikes are much more prevalent under barbiturate anesthesia than in

the awake mouse preparation (see Fig. 2-5c).

85

Detailed Methods

Subjects

Juvenile, male C57/BL6 mice (Charles River Laboratories) were group housed, on a 12

hr/12 hr light/dark cycle, with food and water available ad libitum. All animals were treated

according to NIH and MIT guidelines for animal use.

Surgical preparation for acute in vivo recording

P25 animals were anesthetized with 50 mg/kg ketamine and 10 mg/kg xylazine (i.p.).

Using cyanoacrylate, a fixation post was attached to the skull anterior to bregma. The skull

above the dLGN (2.0 mm posterior to bregma, 2.0 mm lateral to the midline) was demarcated for

future acute recording and the skull surrounding the location was encircled by a plastic ring

affixed with cyanoacrylate. An EEG electrode was placed in occipital cortex, and a reference

electrode was placed in frontal cortex. Electrodes were secured with cyanoacrylate and dental

cement was used to cover the entire skull exposure outside of the plastic ring.

Following surgery animals were monitored for signs of infection or discomfort.

Habituation to the restraint apparatus began ≥ 24 hours post-recovery. Animals remained in the

restraint system for ≥ 3 habituation sessions lasting ≥ 30 minutes each prior to the acute

recording session.

Acute recording

Animals were placed in the restraint apparatus and anesthetized with Isoflurane (1.5-

3.0% in 100% oxygen). A craniotomy was performed over the dLGN of one hemisphere.

Isoflurane was discontinued and the animal was allowed to recover from anesthesia while

remaining in the restraint system. A recording bundle consisting of seven 0.009” (22.86 μm)

diameter microwires was lowered into the dLGN. Microwires were 99.95% tungsten with

86

Formvar coating (California Fine Wire Co., Grover Beach, CA). Bundles were hand-made and

surrounded by a fine coat of cyanoacrlate to prevent splaying. Visually-driven unit activity was

used to aid in the placement of the bundle. Single unit activity and simultaneous EEG activity

were recorded using “Recorder” system software (Plexon, Inc., Dallas, TX). Single units were

discriminated offline using “Offline Sorter” (Plexon, Inc.) following methods adapted from

Csicsvari et al. (1998). The digital signal was high-pass filtered at 300 Hz. A recording

threshold was manually placed at a level to minimize noise without losing spikes. Spike

waveforms were sampled at 40 kHz and extended from 500 μs prior to threshold crossing to

1000 μs after crossing. Files recorded before and after visual manipulation were combined prior

to spike sorting to “blind” the user to the condition of each spike. Spike waveforms were aligned

to the peak. Principle component analysis (PCA) was applied to the waveform shapes.

Graphically, units were identified in either two or three dimensions using the first three principle

components, and hand-drawn polygons were used to define the cluster borders. Single units

were required to show refractory periods of at least 1 ms. In order to confirm the same unit was

recorded during the baseline and post-manipulation recording session, the cluster was visualized

in three dimensions using the first two principal components and time. Clusters that were not

stable through time were not included in further analysis. In experiments where the animals

were anesthetized throughout the recording session (see Fig. 2-10) Nembutal was administered

s.c. (100 mg/kg).

Chronic recordings

Once the recording bundle was placed in the contralateral shell of the dLGN (as

described above), it was affixed in place with dental cement to allow for chronic recordings.

87

Stimulus delivery

The visual stimuli were generated using custom MATLAB software (The Mathworks,

Natick, MA) and the Psychophysics Toolbox function set (psychtoolbox.org). The video

monitor, suitably linearized by -correction, was positioned 16 cm from the subjects’ eyes and

centered on the midline, occupying 82.5° x 100° of the visual field. Visual stimuli consisted of

full-screen sinusoidal gratings (0.05 cycles/°, 100% contrast), alternating in phase (phase

reversed) at a temporal frequency of 1, 2 and 4 Hz in both horizontal and vertical orientations.

Animals also viewed natural scene movies (excerpts from Microcosmos: Le peuple de l’herbe,

presented in grayscale, consisting of one 30s segment shown 10 times and one 4 minute segment

shown twice) between presentations of the sinusoidal gratings, with all stimuli presented in a

pseudorandom fashion.

Eyelid closure

Mice were anesthetized by inhalation of Isoflurane (1.5 – 3.0% in 100% oxygen).

Eyelids were held closed with Vetbond tissue adhesive (3M, St. Paul, MN).

Acute TTX injection

Mice were anesthetized by inhalation of Isoflurane (see above). A small puncture was

made in the vitreous chamber using a 30-guage needle. TTX (1 µL, 1 mM, Sigma, St. Louis,

MO) was then injected into the vitreous chamber using a microsyringe (10 µl, Hamilton Co,

Reno, NV). Following syringe withdrawal, the eye was rinsed with sterile eye drops. The

efficacy of retinal blockade by TTX was confirmed by a tonic and fully dilated pupil.

Chronic TTX injection

Under administration of Isoflurane anesthesia (1.5 – 3.0% in 100% oxygen), the superior

part of the conjunctiva was exposed, and the eyeball was stabilized with a 7-0 silk suture.

88

Ophthalmic ointment was applied to keep the eye moist. A small puncture was made in the

vitreous chamber using a 30-guage needle. A glass micropipette attached to a microinjection

apparatus (MMP, World Precision Instruments, Sarasota, FL) was inserted. TTX (300 nL, 2.85

mM, Sigma, St. Louis, MO, in NeuroSeal, NeuroNexus Technologies, Ann Arbor, MI) was

injected into the vitreous chamber. Calcium chloride was then applied at the insertion site to

polymerize the NeuroSeal. Following micropipette withdrawal, the eye was rinsed with sterile

eye drops.

Comparison of pre- and post-manipulation firing rates and burst percentages

Three groups of neurons were recorded under baseline and post-manipulation conditions.

To determine the statistical significance of any manipulation-induced firing rate or burst

percentage changes a two step procedure was used. We first tested if the median firing rate

change differed significantly between groups using a non-parametric Kruskal-Wallis (KW) test.

Second, we tested if the median firing rate change for each of the groups was significantly

different from zero using non-parametric Wilcoxon Sign-Rank (WSR) tests. Non-parametric

tests were used as the data were not normally distributed.

Comparison of pre- and post-manipulation ISI distributions

Mean ISI distributions for each group and condition were constructed using a

bootstrapping method and compared using Kolmogorov-Smirnov (KS) tests. First, for each

group, a neuron was randomly selected with replacement. Next an ISI was randomly selected

with replacement from the set of all ISIs recorded from that neuron. This procedure was

repeated N times where N was the mean number of ISIs recorded from all neurons in the group.

The resulting set of ISIs, a random sample of the group’s mean ISI distribution, was subjected to

a KS test. This procedure was repeated 1000 times, and the mean of the p-value recorded.

89

Comparison of pre- and post-manipulation pairwise correlation

To determine if correlated firing, beyond that predicted by chance, existed between pairs

of neurons, the Bernoulli correlation coefficient between the neurons’

spike trains was calculated (Aertsen et al., 1989). ( ) is the firing probability per bin of

neuron 1 (2) and the probability of joint firing at time lag . Positive (or negative)

departures of from zero indicate synchronous (or anti-synchronous) firing. To check the

statistical significance of manipulation induced correlation changes, the change in across each

lag was calculated and summed over lags from -10 ms to 10 ms for each pair of neurons before

and after manipulation. KW and WSR tests were then applied as above.

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Rittenhouse CD, Siegler BA, Voelker CC, Shouval HZ, Paradiso MA, Bear MF (2006) Stimulus

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Chapter 3

LGN ACTIVITY DURING OCULAR DOMINANCE

PLASTICITY IN THE ADULT MOUSE

93

Abstract

The juvenile brain is generally considered to be more plastic than the brain of an adult.

Binocular neurons in the primary visual cortices (V1) of young animals lose their responsiveness

to an eye deprived of vision by monocular lid closure (MC) (Wiesel and Hubel, 1963). This

ocular dominance (OD) plasticity is a classic example of experience-dependent cortical plasticity

that is traditionally thought to occur only in juvenile animals (Hubel and Wiesel, 1970). While

the end of this “critical period” for OD plasticity has been proposed to be related to maturation of

inhibition in the cortex (reviewed in Hensch, 2004), changes in presynaptic input to V1 could

also contribute to differences in plasticity between juvenile and adult plasticity. Current research

suggests that mice do not have a critical period for OD plasticity, as mouse V1 is susceptible to

OD plasticity into adulthood (reviewed in Hofer et al., 2006a). However, qualitative differences

between juvenile and adult OD plasticity have been observed (Frenkel et al., 2006). To

determine if changes in input patterns to V1 contribute to differences in juvenile and adult

plasticity, we recorded extracellulary from neurons in the dorsal lateral geniculate nucleus

(dLGN) of adult animals during MC and compared the results to findings from juveniles

(Chapter 2). Both juveniles and adults show a similar decrease in correlative firing following lid

closure. However, MC in adults leads to a reduction in burst activity, a difference we were

unable to observe in juveniles at a level of statistical significance. These findings suggest that

presynaptic input to V1 from dLGN may contribute to differences in juvenile and adult cortical

plasticity. This may have important implications in studies of “critical period” mechanisms for

experience-dependent cortical plasticity and may also be important in the treatment of disorders

including amblyopia.

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Introduction

OD plasticity is a well-studied model of experience-dependent cortical plasticity.

Closing one eye (MC) for short periods of time in young animals leads to changes in V1, such

that the majority of binocular neurons show responses now dominated by the non-deprived eye

(Wiesel and Hubel, 1963). This OD shift results primarily from a loss of response to deprived-

eye input (Mioche and Singer, 1989; Frenkel and Bear, 2004). The critical period for the loss of

deprived-eye response is restricted to early postnatal life in kittens and higher mammals (Hubel

and Wiesel, 1970). Cortical changes, including a maturation of inhibitory circuitry have been

put forth as putative mechanisms which lead to the close of the critical period (Kirkwood and

Bear, 1994; Huang et al., 1999; Rozas et al., 2001).

Recent studies suggest the mouse does not have a critical period as classically defined

(reviewed in Hofer et al., 2006a). For example, changes in the expression of the immediate early

gene, Arc, indicating an expansion of non-deprived eye input, are present following MC in mice

as old as 13 weeks, well past the traditional end of the critical periods (Tagawa et al., 2005).

Adult OD plasticity has also been demonstrated with intrinsic signal optical imaging and

extracellular single-unit recording techniques, although the OD shift observed in this study

resulted from potentiation of the non-deprived eye input, not a depression of the deprived eye

response (Hofer et al., 2006b). Furthermore, visually evoked potential recordings in the adult

mouse demonstrate that extended periods of MC (7 days) lead to both deprived-eye depression

and open-eye potentiation, while, unlike the case in juveniles, shorter periods of MC (3 days) are

not accompanied by a loss of the deprived-eye response (Frenkel et al., 2006). While these

findings suggest that the concept of a “critical period,” as classically defined, may not exist in the

mouse, there is a qualitative difference between juvenile and adult OD plasticity in this species.

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The goal of this study was to compare the effects of MC in juveniles and adults to

determine if ages differences in OD plasticity can be accounted for by presynaptic mechanisms,

in addition to possible changes in cortex. We have already shown that MC in juveniles results in

decreased correlative firing of simultaneously recorded dLGN neurons (Chapter 2). This lack of

correlated input to V1 presumptively leads to deprived-eye depression via long-term depression

(LTD)-like mechanisms (Heynen et al., 2003; Yoon et al., 2008). Therefore, we recorded dLGN

activity in the awake, adult mouse preparation, to see if similar changes in firing pattern occur

during MC.

Methods Summary

Adult male mice were anesthetized and surgically prepared for head restraint and

electrophysiological recordings (see Detailed Methods). The animals were behaviorally

habituated to restraint over the next several days, and bundles of electrodes were inserted in the

dLGN when the mice were between the ages of postnatal day (P) 90 and P130. The location of

the recording bundle in the dLGN was initially determined by observation of visually-driven unit

activity in response to stimulation of the contralateral eye, and was always subsequently

confirmed histologically (Fig. 3-1). Baseline recordings were made with the eye contralateral to

the electrode viewing and the ipsilateral eye occluded. Activity approximating that during

normal visual experience was recorded in response to phase-reversing sinusoidal gratings and

natural scene stimuli (see Methods). Unless otherwise indicated, results using grating stimuli are

illustrated. Following the baseline recording session, animals were briefly anesthetized and

eyelid closure (MC) was performed. After 30 minutes of recovery from anesthesia, stimuli were

presented for a second recording session. At the conclusion of recordings, an electrolytic lesion

was made at the recording site to allow for histological confirmation of electrode placement.

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Results

Figure 3-2 shows the responses of two simultaneously recorded dLGN neurons, with

different baseline firing rates, before and after MC. Prior to lid closure, both neurons are

visually responsive to the grating stimulus, as indicated by an increased likelihood of observing a

spike following phase reversal of the stimulus (indicated by arrowhead). Closing the lid causes

both neurons to lose their visually driven activity. However, the overall amount of spikes does

not change dramatically during MC.

To quantify the effect of MC on firing rate, we measured the overall amount of spiking

activity throughout the recording session before and after MC (Fig. 3-3). As observed in the

juveniles (Chapter 2, reproduced in Fig. 3-3, inset), MC has no significant effect on overall firing

rate (p > 0.1, Wilcoxon Sign-Rank (WSR)). Additionally, there are no significant differences

between the firing rates of juveniles and adults in the baseline condition (p > 0.1, Mann-Whitney

U test (MWU)), or in the degree of change in juveniles and adults following MC (p > 0.7,

MWU).

While firing rate provides information about the overall spiking activity of the neuron, it

does not provide information about temporal patterns within the spike train. One type of spike

pattern common among thalamic nuclei is the thalamic burst (reviewed in Sherman, 2001).

Bursts are detected in extracellular recordings when spike patterns consist of at least 100 ms of

silence followed by at least two spikes with inter-spike intervals less than 4 ms (Lu et al., 1992).

In the awake, juvenile mouse dLGN we found no change is the percentage of spikes in bursts

following MC (Chapter 2, reproduced in Fig. 3-4, inset). In the adult dLGN however, MC did

significantly reduce the burst percentage (Fig. 3-4; p < 0.04, WSR). We did not however, find a

difference between juveniles and adults either before MC (p > 0.2, MWU) or in the degree of

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change following MC (p > 0.8, MWU). This suggests subtle differences between juveniles and

adults in both the baseline burst percentage, and the percentage of spikes in bursts during MC.

While we were able to detect differences between normal viewing and MC when looking

at single spike train properties in the adult, this was not present in young animals. In juveniles,

the most robust change in neural activity during MC was a change in the amount of correlative

firing between pairs of simultaneously recorded neurons (Chapter 2). Therefore, we performed

similar analyses in the adult. Figure 3-5a shows the change in cross-correlations for a

representative pair of simultaneously recorded neurons before and after MC. A scatter plot

depicting the amount of correlative firing before and after MC for the entire data set, including

juvenile animals, is shown in figure 3-5b. (Juvenile data from Chapter 2 reproduced for

comparison.) MC causes a significant decrease in correlative firing in adults (p < 0.05, WSR)

and juveniles, with no significant difference between the two groups in the baseline condition (p

> 0.3, MWU) or in the amount of change in correlative firing between juveniles and adults

following MC (p > 0.4, MWU).

Because full-field sinusoidal gratings may be unnatural visual stimuli, as in the juvenile

study (Chapter 2), we also showed the animal natural scene movies before and after MC. Figure

3-6 shows the results for activity collected while the adult animals viewed the movies. While the

firing rate (Fig. 3-6a) and burst percentage results (Fig. 3-6b) were the same as with the grating

stimuli, the decrease in correlation following MC is not reach statistically significance (p > 0.06,

WSR). The difference between the grating and natural scene conditions appears to be due to a

difference in the amount of correlation during the baseline condition (p < 0.02, WSR) as there is

less correlative activity during natural scene viewing, with no difference during MC (p > 0.1,

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WSR) (data not shown). This may be a result of less receptive field overlap in the adult dLGN,

possibly as a result of receptive field refinement as the animal continues to develop.

Discussion

The effects of MC on dLGN firing activity are subtlety different between juvenile and

adult mice. While MC results in no change in firing rate in both juveniles and adults, it does

cause a decrease in the percentage of spikes in bursts in adults that was not observed in juveniles.

Furthermore, the decrease in correlative firing between pairs of simultaneously recorded neurons

following MC is observed in both juveniles and adults when assessed using sinusoidal grating

stimuli, but is not present in adults when assessed using natural scene movies, as it was in

juveniles.

While the decrease in burst percentage following MC was not seen in the juvenile, this is

not a surprising finding. Literature from both the cat (Lesica and Stanley, 2004) and mouse

(Grubb and Thompson, 2005) suggests that bursts convey visual information. Both of these

studies observed an increase in burst percentage when the animal was presented with visual

stimuli, as opposed to viewing white noise stimuli (Lesica and Stanley, 2004) or a blank screen

(Grubb and Thompson, 2005).

There are several potential reasons for the difference in dLGN bursting between juvenile

and adult animals. Bursts result from the activation of T-Type Ca2+

channels, which require a

long period of hyperpolarization to deinactivate. Following deinactivation, a small membrane

depolarization will activate the channel, eliciting the burst (Sherman, 2001). The difference

between adults and juveniles may result from changes in the conditions that allow for the

hyperpolarization and/or changes in the potential sources of subsequent depolarization.

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Completely eliminating retinal input via intraocular injection of tetrodotoxin (TTX)

increases the percentage of spikes in bursts (Chapter 2). Therefore, if retinal activity were higher

in adults than juveniles during MC, bursts may occur less often because the conditions for

extended periods of hyperpolarization are less prevalent. Retinal activity may be higher in adults

if there is a developmental change in the amount of spontaneous retinal activity or if adults can

“see” better through the eyelid. Conversely, if juveniles can “see” better through the eyelid,

visually driven bursts may be more prevalent during MC. Additionally, the other sources of

input to the dLGN may be less active, thus less likely to activate the Ca2+

channel. The dLGN

receives the majority of its input from cortical feedback. Thus, the well-documented maturation

of cortical inhibition may decrease cortical feedback in the adult, which may in turn lead to less

bursting (reviewed in Hensch et al., 1998).

In addition to the bursting results, both juveniles and adults showed similar decreases in

correlative firing when assessed with sinusoidal gratings. Therefore, this may be an important

contribution to OD plasticity observed throughout the animal’s life. However, adults did not

show a statistically significant decrease in correlative firing when assessed with natural scene

movies. This may explain why deprived-eye depression is reported as either absent (Hofer et al.,

2006b) or slower (Frenkel et al., 2006) in adults. The difference between the sinusoidal grating

and natural scene stimuli in the baseline condition could result from a development of more

refined receptive fields in adults, thus decreasing the amount of correlation observed during the

movie. Cats show a maturation of spatio-temporal receptive field properties that persists past the

end of the critical period for OD plasticity in kittens (Cai et al., 1997). This type of study has not

been conducted in the mouse, but may explain this difference between juveniles and adults.

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Alternatively, the sample size for the data set may just be too low to detect a statistically

significant decrease.

Regardless of the cause for the differences in dLGN activity between adults and

juveniles, the existence of these subtle differences suggest that changes in presynaptic activity

between juveniles and adults may be an important factor contributing to the qualitative and

quantitative differences in cortical plasticity with age. It is possible that the mechanisms for

juvenile plasticity depend heavily on the de-correlation between inputs from the deprived eye,

while in adulthood, the decrease in bursting activity, alone or in conjunction with a decrease in

correlative activity, is the main driver of deprived-eye depression. This study, in addition to

previous cortical studies, suggests that the changes in OD plasticity as an animal matures involve

an interplay between differences in input patterns to cortex and changes within cortex itself.

Moreover, this may be a principle governing maturation of plasticity throughout the cortex, and

may be an important consideration when designing treatments for disorders such as amblyopia.

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Figure 3-1

Figure 3-1 Methodology.

a, Experimental setup. Data were obtained from awake, head-restrained mice viewing grating

stimuli. b, Schematic of recording electrode placement in the dLGN

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Figure 3-2

Figure 3-2 dLGN responses before and after visual manipulation.

a - b, Peristimulus time histograms and raster plots from two simultaneously recorded neurons

(80 stimulus trials) are presented. Stimuli were presented at 0° or 90°, 1 Hz phase reversing

(▼). Arrowheads in this and subsequent figures indicate time of stimulus phase reversal. Spike

waveforms are recording session averages. Scale bar: 100 µV, 500 µs. Left column data were

obtained during baseline, right column after lid closure. Note the loss of visual responsiveness

with MC, and that the total number of spikes recorded is comparable before and after each

manipulation.

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Figure 3-3

Figure 3-3 dLGN firing rate before and after MC in adult mice.

Monocular lid closure has no effect on dLGN firing rate in adult animals (n = 18 neurons (6

animals), p > 0.1 Wilcoxon Sign-Rank (WSR)). Firing rate was calculated for the full duration

of visual stimulation across all stimulus conditions. Connected gray circles represent the same

neuron recorded before and after eye manipulation ((O)pen, (C)losed). Black lines indicate

median values. Inset: Results from juvenile MC experiment (Fig. 2-3: n = 24 neurons (12

animals), p > 0.3). There are no significant differences between the firing rates of juveniles and

adults in the baseline condition (p > 0.1, Mann-Whitney U test (MWU)), or in the degree of

change in juveniles and adults following MC (p > 0.7, MWU). See also Fig. 3-6a.

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Figure 3-4

Figure 3-4 Percentage of spikes in burst before and after MC in adult dLGN.

Monocular lid closure decreases the percentage of spikes in bursts in adults (n = 18 neurons (6

animals), p < 0.04 Wilcoxon Sign-Rank (WSR)). Connected gray circles represent the same

neuron recorded before and after eye manipulation ((O)pen, (C)losed). Black lines indicate

median values. Inset: Results from juvenile MC experiment (Fig. 2-5c: n = 24 neurons (12

animals), p > 0.2). There is no significant difference between juveniles and adults either before

MC (p > 0.2, MWU) or in the degree of change following MC (p > 0.8, MWU). See also Fig. 3-

6b.

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Figure 3-5

Figure 3-5 Monocular lid closure decreases correlative dLGN firing in adult animals.

a, Cross-correlograms from a pair of simultaneously recorded neurons before (black line) and

after (gray line) MC. b, Scatter plots of the area under the cross-crosscorrelogram before and

after MC for each pair of simultaneously recorded neurons. Filled circles are pairs recorded in

adult mice, open circles from juveniles (reproduced from Fig. 2-8b). Gray line represents unity.

Note that following MC nearly all points fall below the unity line, indicating a decrease in

correlative firing. See also Fig. 3-6c. c, MC decreases spike correlation (adults: n = 14 neuron

pairs (6 animals), p < 0.05 WSR; juveniles (Fig. 2-8c): n = 20 neuron pairs (6 animals), p < 10-3

).

Bars represent the median change in area under the peak of the cross-correlogram (± 10 ms)

following visual manipulation. Error bars show the interquartile range. There is no significant

difference between the two groups in the baseline condition (p > 0.3, MWU) or in the amount of

change in correlative firing between juveniles and adults following MC (p > 0.4, MWU). See

also Fig. 3-6d.

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Figure 3-6

Figure 3-6 The effects of monocular lid closure on dLGN activity when assessed using natural

scene stimuli are comparable to that observed using phase-reversing sinusoidal grating stimuli.

a, Monocular lid closure (C) has no effect on dLGN firing rate (n = 18 neurons (8 animals), p >

0.3 WSR). Connected gray circles represent the same neuron recorded before and after eye

manipulation. Black lines indicate the median values. b, MC decreases the percentage of spikes

in bursts (n = 18 neurons (8 animals), p < 0.05) c, Scatter plots of the area under the cross-

crosscorrelogram before and after MC for each pair of simultaneously recorded neurons. Note

that following MC many of the points fall below the unity line, indicating a decrease in

correlative firing. d, A decrease in spike correlation is observed following MC, although this did

not reach statistical significance (n = 14 neuron pairs (6 animals), p > 0.06). Bars represent the

median change in area under the cross-correlogram following visual manipulation. Error bars

show the interquartile range.

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Detailed Methods

Subjects

Adult, male C57/BL6 mice (Charles River Laboratories) were group housed, on a 12

hr/12 hr light/dark cycle, with food and water available ad libitum. All animals were treated

according to NIH and MIT guidelines for animal use. Methods for obtaining data from juvenile

mice are described in Chapter 2 – Detailed Methods.

Surgical preparation for acute in vivo recording

P90 – P125 animals were anesthetized with 50 mg/kg ketamine and 10 mg/kg xylazine

(i.p.). Using cyanoacrylate, a fixation post was attached to the skull anterior to bregma. The

skull above the dLGN (2.0 mm posterior to bregma, 2.0 mm lateral to the midline) was

demarcated for future acute recording and the skull surrounding the location was encircled by a

plastic ring affixed with cyanoacrylate. An EEG electrode was placed in occipital cortex, and a

reference electrode was placed in frontal cortex. Electrodes were secured with cyanoacrylate and

dental cement was used to cover the entire skull exposure outside of the plastic ring.

Following surgery animals were monitored for signs of infection or discomfort.

Habituation to the restraint apparatus began ≥ 24 hours post-recovery. Animals remained in the

restraint system for ≥ 3 habituation sessions lasting ≥ 30 minutes each prior to the acute

recording session.

Acute recording

Animals were placed in the restraint apparatus and anesthetized with Isoflurane (1.5-

3.0% in 100% oxygen). A craniotomy was performed over the dLGN of one hemisphere.

Isoflurane was discontinued and the animal was allowed to recover from anesthesia while

remaining in the restraint system. A recording bundle consisting of seven 0.009” (22.86 μm)

114

diameter microwires was lowered into the dLGN. Microwires were 99.95% tungsten with

Formvar coating (California Fine Wire Co., Grover Beach, CA). Bundles were hand-made and

surrounded by a fine coat of cyanoacrlate to prevent splaying. Visually-driven unit activity was

used to aid in the placement of the bundle. Single unit activity and simultaneous EEG activity

were recorded using “Recorder” system software (Plexon, Inc., Dallas, TX). Single units were

discriminated offline using “Offline Sorter” (Plexon, Inc.) following methods adapted from

Csicsvari et al. (1998). The digital signal was high-pass filtered at 300 Hz. A recording

threshold was manually placed at a level to minimize noise without losing spikes. Spike

waveforms were sampled at 40 kHz and extended from 500 μs prior to threshold crossing to

1000 μs after crossing. Files recorded before and after visual manipulation were combined prior

to spike sorting to “blind” the user to the condition of each spike. Spike waveforms were aligned

to the peak. Principle component analysis (PCA) was applied to the waveform shapes.

Graphically, units were identified in either two or three dimensions using the first three principle

components, and hand-drawn polygons were used to define the cluster borders. Single units

were required to show refractory periods of at least 1 ms. In order to confirm the same unit was

recorded during the baseline and post-manipulation recording session, the cluster was visualized

in three dimensions using the first two principal components and time. Clusters that were not

stable through time were not included in further analysis. All animals were between P90 and

P130 on the day of recording.

Stimulus delivery

The visual stimuli were generated using custom MATLAB software (The Mathworks,

Natick, MA) and the Psychophysics Toolbox function set (psychtoolbox.org). The video

monitor, suitably linearized by -correction, was positioned 16 cm from the subjects’ eyes and

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centered on the midline, occupying 82.5° x 100° of the visual field. Visual stimuli consisted of

full-screen sinusoidal gratings (0.05 cycles/°, 100% contrast), alternating in phase (phase

reversed) at a temporal frequency of 1, 2 and 4 Hz in both horizontal and vertical orientations.

Animals also viewed natural scene movies (excerpts from Microcosmos: Le peuple de l’herbe,

presented in grayscale, consisting of one 30s segment shown 10 times and one 4 minute segment

shown twice) between presentations of the sinusoidal gratings, with all stimuli presented in a

pseudorandom fashion.

Eyelid closure

Mice were anesthetized by inhalation of Isoflurane (1.5 – 3.0% in 100% oxygen).

Eyelids were held closed with Vetbond tissue adhesive (3M, St. Paul, MN).

Comparison of pre- and post-manipulation firing rates and burst percentages

Three groups of neurons were recorded under baseline and post-manipulation conditions.

To determine the statistical significance of any manipulation-induced firing rate or burst

percentage changes a two step procedure was used. We first tested if the median firing rate

change differed significantly between groups using a non-parametric Kruskal-Wallis (KW) test.

Second, we tested if the median firing rate change for each of the groups was significantly

different from zero using non-parametric Wilcoxon Sign-Rank (WSR) tests. Non-parametric

tests were used as the data were not normally distributed.

Comparison of pre- and post-manipulation pairwise correlation

To determine if correlated firing, beyond that predicted by chance, existed between pairs

of neurons, the Bernoulli correlation coefficient between the neurons’

spike trains was calculated (Aertsen et al., 1989). ( ) is the firing probability per bin of

neuron 1 (2) and the probability of joint firing at time lag . Positive (or negative)

116

departures of from zero indicate synchronous (or anti-synchronous) firing. To check the

statistical significance of manipulation induced correlation changes, the change in across each

lag was calculated and summed over lags from -10 ms to 10 ms for each pair of neurons before

and after manipulation. KW and WSR tests were then applied as above.

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visual experience in mouse visual cortex. Neuron 51:339-349.

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mouse dorsal lateral geniculate nucleus. J Neurophysiol 93:3224-3247.

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Hofer SB, Mrsic-Flogel TD, Bonhoeffer T, Hubener M (2006a) Lifelong learning: ocular

dominance plasticity in mouse visual cortex. Curr Opin Neurobiol 16:451-459.

Hofer SB, Mrsic-Flogel TD, Bonhoeffer T, Hubener M (2006b) Prior experience enhances

plasticity in adult visual cortex. Nat Neurosci 9:127-132.

Huang JZ, Kirkwood A, Pizzorusso T, Porciatti V, Bear MF, Maffei L, Tonegawa S (1999)

Brain-derived neurotrophic factor is a key regulator of visual cortical development and

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Hubel DH, Wiesel TN (1970) The period of susceptibility to the physiological effects of

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Kirkwood A, Bear MF (1994) Hebbian synapses in visual cortex. J Neurosci 14:1634-1645.

Lesica NA, Stanley GB (2004) Encoding of natural scene movies by tonic and burst spikes in the

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Lu SM, Guido W, Sherman SM (1992) Effects of membrane voltage on receptive field properties

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Chapter 4

DISCUSSION: Methodological Considerations,

Implications and Future Studies

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The purpose of this research was to characterize dLGN activity patterns during normal

viewing, monocular lid closure (MC), and monocular retinal inactivation (MI) in the awake

animal. We were essentially testing the hypothesis that the dLGN was a faithful relay of retinal

input, such that when we closed the eye the dLGN would be “noisy” and less active, reflecting

spontaneous retinal input, and after MI the dLGN would, like the retina, be silent. However, we

discovered that the activity patterns of dLGN neurons are not the direct reflection of retinal

input. Neither MC nor MI decreased the overall firing rate in dLGN. MC led to a decrease in

the correlative firing of simultaneously recorded neurons, while MI led to an increase in bursting

activity and concomitant increase in correlative firing. In adults, MC reduced the bursting

activity in addition to de-correlating the activity of simultaneously recorded neurons. These

results have several implications for understanding cortical plasticity. After discussing the

methodological considerations of these experiments, I will summarize some implications and

describe avenues for future experiments.

Methodological Considerations

As with any scientific endeavor, compromises are often made in experimental design.

This research was no exception, and there are several caveats to the methodology worthy of

consideration.

The majority of the experiments were conducted using acute recording methods. The

major advantage to this strategy is that it is easier to obtain dLGN units for recording in an acute

preparation. The main drawback however, is that we were only able to look at the effects of

deprivation within hours of manipulation. While this was less than ideal, noticeable changes in

OD scores have been observed with as little as 6 hrs of deprivation (Mioche and Singer, 1989),

so the activity we were recording should correspond to the neural activity that does in fact drive

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OD plasticity. A chronic recording method would have allowed us to explore the effects over

many hours to days of deprived eye viewing conditions. It is possible that dLGN activity

changes with increased periods of deprivation, and we were unable to track those changes with

the acute preparation. Because we suspected that dLGN activity may have changed over time

during MI, we did perform chronic recordings during that manipulation. However, the activity

did not change in any qualitative or quantitative way. Therefore, we assume a similar case

would hold for animals undergoing MC.

Another advantage to the acute recording preparation is that we believe we were able to

hold the same neurons before and after visual manipulation. To ensure that we recorded from

the same neuron, spike sorting was conducted blind to the source of the spike (pre- or post-

manipulation). Clusters were then checked for consistency between recording sessions. Because

we recorded nearly continuously throughout the experiment, stable clusters indicate that we were

holding the same unit throughout the recording sessions. This allowed us to use paired statistics

when analyzing the data. Had we used a chronic preparation and sampled activity over several

days, we could not be certain that we were recording from the same unit across days, and

statistically comparisons would have been unpaired, between groups.

Another methodological consideration related to the use of acute recordings is the choice

of head-fixing the animals. The head-fixed preparation provides a marked advantage over

freely-moving recordings because we can control the visual stimuli presented to the animal.

However, chronically implanted electrodes would have allowed us to record from animals

exploring the environment in the same manner to that which occurs during traditional OD

experiments. Additionally, we could have recorded from the animal during sleep states to see if

dLGN activity during sleep was affected by the visual manipulations. One final drawback to the

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acute, head-fixed recording technique is that we were unable to suture the eyelids without

compromising the electrode position. For this reason, we used surgical glue to close the eyelid,

approximating lid suturing.

Another important consideration was that we were unable to measure receptive fields

(RFs) from the neurons we were recording. Mapping the RFs would have provided us with

valuable information, particularly for the analysis of simultaneously recorded neurons.

However, because the animals were awake and could move their eyes, it was not feasible to map

RF location. One option would have been to anesthetize the animal during RF mapping.

However, without also paralyzing the animals there may still have been eye movements, and

paralyzing and respirating the animal may have affected the subsequent awake recording

sessions. Another option would have been to track the eye movements of the mouse to attempt

to map RFs in the awake animal, but this was not attempted.

We also unable to confirm that the dLGN neurons we were recording from were in fact

relay cells, and not inhibitory interneurons. To address this issue, we did attempt to validate that

the neurons were projecting to primary visual cortex by stimulating either V1 or the optic

radiations and looking for an antidromic response in dLGN. However, we never observed single

units responsive to the antidromic stimulation. Therefore, it is possible that interneurons were

included in our data set, but because they account for only approximately 20% of the neuronal

population (Arcelli et al., 1997) and they have smaller cell bodies (Sherman and Guillery, 2006)

implying we would record from them less often, it is unlikely they made a significant

contribution to our findings.

Another concern relates to our spike sorting method. Because our spike sorting method

is somewhat subjective, our analysis will be subject to both type I and type II errors. That is, we

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will sometimes include false spikes in our data in addition to sometimes omitting spikes. These

errors can contribute to changes in firing rate and ISI distributions due to simple changes in the

amount of spikes. They will also contribute to changes in burst percentage because spurious

spikes during periods of quiescence may prevent the observation of a burst, false spikes during

bursts would falsely inflate the burst percentage, missed spikes during quiescence could lead to

false classification of bursts, and missed spikes during bursts would decrease the burst

percentage. However, because we are blind to the condition of the spikes (pre- or post-

manipulation), both conditions will be equally affected by these errors. Furthermore, we looked

at relative differences before and after manipulation, and since both conditions should be equally

affected, the relative differences should not change. While our absolute values may vary slightly

from the true values, we do not make claims about absolute values.

There are at least two additional controls that we could have performed during our

experiments. One useful control would have been intraocular saline injections. The saline

injection would have controlled for any effects of the pain from intraocular TTX injection.

Following saline injection, we would not expect the bursts that occurred after MI. However, we

do not feel that the bursts observed following intraocular TTX resulted from changes in

behavioral state because our chronic recordings indicate that the bursts persist throughout the

period of MI, up to 48 hours, a time at which the animal should have fully recovered from any

discomfort due to injection.

A second useful control would have been a complete “lights out” condition to record

purely spontaneous activity. Our experimental setup did not allow for this type of recording.

Recording purely spontaneous activity would have allowed us to compare the effect of MC to the

complete absence of visual input.

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Implications

In spite of the drawbacks to our experimental design, there are several implications of our

findings. We will discuss the implications of our results on cortical plasticity in juvenile and

adult mice, the implications of the increase in bursting following MI, and the potential hazards of

using anesthetics when studying dLGN function.

Cortical Plasticity

OD plasticity serves as a model for plasticity throughout the cortex. Deprived-eye

depression is known to mimic and occlude LTD (Heynen et al., 2003). Our results suggest that

the activity driving deprived-eye depression in vivo is a decrease in correlative firing of neurons

in dLGN, not a decrease in overall dLGN firing activity. This implies that cortical LTD in vivo

results from a de-correlation of presynaptic input.

The depression of presynaptic input can theoretically occur as a result of either

homosynaptic or heterosynaptic mechanisms (reviewed in Kerr and Abraham, 1996).

Heterosynaptic LTD occurs when postsynaptic activation is paired with very low levels of

presynaptic activity, while homosynaptic LTD results from active presynaptic synapses causing

their own depression by not correlating with postsynaptic responses. In order to distinguish

between homosynaptic and heterosynaptic mechanisms for deprived-eye depression, Rittenhouse

et al. (1999) compared the effects of MC and MI, but assumed that because MI silenced the

retina, the dLGN was also inactive. Because MC and not MI result in deprived-eye depression,

the mechanism was assumed to be homosynaptic, as weak levels of presynaptic activity did not

depress (MI), but the “noise” during MC did (Blais et al., 1999; Rittenhouse et al., 1999).

Blais et al. (1999) fit the assumptions of the Rittenhouse study to the Bienenstock,

Cooper, and Munro (BCM) (Bienenstock et al., 1982) model for homosynaptic plasticity. One

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feature of this model is that the more residual “noise” in the presynaptic input, the faster that

input will depress. Since the dLGN was assumed to be more “noisy” during MC, and silent

during MI, the greater depression following MC fit well with the model.

However, we now know that dLGN neurons are not silent during MI, and we assume that

there is no deprived-eye depression following MI because the dLGN activity is highly patterned.

While this may invalidate the comparison assumed in the Rittenhouse study (1999), we can still

conclude that deprived-eye depression results from homosynaptic mechanisms. Homosynaptic

LTD is still the likely mechanisms for deprived-eye depression because the overall amount of

input during MC did not change. Heterosynaptic mechanisms function by “punishing” inputs

with decreased activity. MC resulted in a de-correlation of presynaptic input, and this de-

correlated input likely depressed itself, resulting in homosynaptic LTD. The conclusive study

however, described below (Future Experiments), would require the silencing of dLGN input.

The standard protocols for inducing LTD in vivo and in vitro are not physiologically

relevant (reviewed in Holscher, 1999; and Albensi et al., 2007). Because we have recorded the

neural activity that leads to homosynaptic LTD in vivo, this activity could be used to design more

physiologically relevant LTD induction protocols (see Future Experiments). Inducing LTD with

physiologically-derived induction protocols will allow researchers to study the mechanisms of

experience-dependent plasticity that actually occur in vivo. Additionally, these mechanisms may

be conserved throughout cortex. Moreover, because there are subtle differences between

juvenile and adult dLGN activity during MC, which may contribute to the differences in juvenile

and adult OD plasticity, understanding the differences between these input patterns may

elucidate mechanisms of experience-dependent plasticity in the adult. This may also help in the

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development of treatments for disorders such as ambylopia and strabismus, conditions which are

very difficult to treat in adulthood.

MI-Induced Thalamic Bursting

In addition to the studies described above, several other studies assumed that MI

decreased dLGN activity (e.g. Chapman et al., 1986; Greuel et al., 1987; Catalano et al., 1997;

Caleo et al., 1999; Desai et al., 2002; Heynen et al., 2003; Frenkel and Bear, 2004; Young et al.,

2007). The findings of these studies warrant reinterpretation given our results.

Chapman et al. (1986) and Greuel et al. (1987) performed the first studies looking at the

effects of retinal inactivation on OD plasticity (for details, see Chapter 1). These studies found

OD shifts towards the non-deprived eye after extended periods of MI. While they attributed the

shift to a disconnection of deprived-eye input, their experimental design did not allow them to

distinguish deprived-eye depression from potentiation of the non-deprived eye. Based on the

findings of Frenkel and Bear (2004), who show open-eye potentiation following MI, and our

results regarding bursting in both the contralateral and ipsilateral segments of dLGN, we can be

relatively certain that their OD shift was in fact the result of potentiation of the non-deprived eye

input. These studies also looked at the OD shift following MI with lid closure of the fellow

eye. The results of these studies can be reinterpreted if we assume that when one eye undergoes

MI, the fellow eye is driven towards an increase in synaptic strength. Thus in Greuel et al.

(1987), no shift occurs because the increased synaptic strength occludes deprived-eye

depression. In Chapman et al. (1986), the increased synaptic strength surpassed the depressive

effect of MC.

Catalano et al. (1997) showed that NMDA-receptor subunit 1 (NR1) expression was

decreased in layer IV columns of cat and ferret V1 receiving input from the deprived eye during

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MI. They did not observe this decrease in layer II/III, nor was it observed in layer IV of dark-

reared animals. They concluded that NR1 activity was reduced in columns receiving deprived-

eye input because those neurons were silenced. They attribute the silencing of the layer IV

neurons to the lack of dLGN input following MI and local inhibition from non-deprived eye

columns.

However, we know that the dLGN input is not silent, but we do not know the effect of

this input on cortical activity. On the one hand, the increased dLGN bursting observed during

MI may lead to an increase in activity in V1, as bursts are known to enhance efficacy of

thalamocortical (TC) transmission (Swadlow and Gusev, 2001). In fact, even though the

expression of T-Type Ca++

channels is lower in cortex than in the thalamus, hippocampus, and

other regions that highly express this channel required for bursting (Klugbauer et al., 1999), it is

possible that V1 would burst in response to dLGN bursting. However, dLGN bursting may

actually decrease activity in V1. Swadlow and Gusev (2001) looked specifically at TC

connections onto presumptive interneurons and showed that bursting enhanced the probability of

driving inhibitory neurons. Other studies in the barrel cortex have demonstrated that TC neurons

provide a stronger drive to inhibitory neurons than onto excitatory cells (Cruikshank et al.,

2007). Thus, increased inhibition may result in an overall decrease of cortical activity following

MI, and may explain the decreased NR1 expression. Experiments to characterize cortical

activity patterns during MI are described below (Future Experiments).

Desai et al. (2002) looked at the effects of synaptic scaling following MI in the

monocular segment of V1 in rats. They found an increase in mini excitatory postsynaptic

potential (mEPSP) amplitude in layer IV neurons following MI from postnatal day (P)14 – P16.

From P21 – P23, MI resulted in no change in mEPSPs in layer IV, while an increase in mEPSPs

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was observed in layer II/III. They conclude that this scaling results from homeostatic changes

due to lack of dLGN input during MI, and that this scaling is lost at P21 due to undetermined

“critical period” mechanisms. There are some experimental differences between these studies

and ours that may account for the differences. They were examining activity at a much younger

age and were examining plasticity in the monocular segment of V1. While it is possible that MI

does not result in dLGN bursting at this age, these results could be interpreted with the presence

of a bursting input as well. Again, if bursting input leads to the silencing of cortical activity due

to increased inhibition, synaptic scaling may result. Moreover, the observed synaptic scaling

may actually be a potentiation of the response due to the increase in bursting. Furthermore, if

there is a developmental increase in bursting, perhaps bursting is not robust prior to P21, but

does drive layer IV activity after that time. This would explain the lack of scaling in layer IV,

while activity from IV to II/III may be decreased during bursting. Again, recordings of cortical

activity during MI are necessary to confirm these hypotheses.

Frenkel and Bear (2004), as described above, saw a lack of deprived-eye depression

accompanied by an increase in the non-deprived eye response following MI in the mouse. While

they interpreted this finding as a lack of depression due to the absence of retinal input, we now

know the lack of depression is likely due to patterned dLGN input following MI, with the

ipsilateral potentiation likely resulting from bursting in the ipsilateral core of the dLGN. Heynen

et al. (2003) similarly misinterpreted the lack of AMPAR endocytosis following MI as resulting

from a lack of dLGN input, when in fact the bursting presynaptic input would be highly unlikely

to result in LTD-like changes in V1.

Young et al. (2007) have modeled the effects on cortical receptive field properties

following retinal lesions. Although induction of small retinal lesions is different than MI, it is

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unknown if retinal lesions decrease dLGN activity, or like MI, increase dLGN bursting. Because

their model assumed that the lesion resulted in the absence of presynaptic input to V1, it is

unknown if their conclusions regarding the nature of intracortical plasticity following lesioning

would hold. Without access to the specifics of their model, it is hard to predict the effect of

bursting input on their conclusions, but presumably changing the nature of the cortical input in

their model may alter their conclusions.

As is apparent from these examples, our work elucidating the activity in dLGN neurons

during MI provides critical insight to the interpretations of studies that have used this type of

manipulation. Hopefully this will lead to an increased understanding of the mechanisms of

cortical plasticity.

Effects of Anesthesia

Another critical finding of our work was that dLGN activity under barbiturate anesthesia

does not accurately represent the activity observed in the awake animal. We replicated the

results from anesthetized studies by recording from Nembutal-anesthetized mice (Chapter 2).

Under anesthesia, almost all dLGN activity was visually driven, with very little residual

spontaneous activity. Furthermore, visual stimulation often resulted in bursting activity, with

approximately 20% of spikes in bursts, similar to what was observed by Grubb and Thompson

(2005). Moreover, MI in the anesthetized preparation nearly eliminated all spiking activity,

confirming previous studies in the cat (Stryker and Harris, 1986; Rittenhouse et al., 1999). Most

importantly though, none of these results accurately reflect our recordings from awake mice,

where only approximately 5% of spikes were in bursts under normal viewing conditions, and MI

had no effect on overall firing rate, but significantly increased dLGN bursting.

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In Chapter 1, I describe a conflict between studies showing that the TC synapse is

tonically depressed (Swadlow et al., 2002; Jia et al., 2004) and studies showing paired-pulse

facilitation at this synapse (Usrey et al., 2000). One distinguishing factor between the two

findings is the type of anesthesia used in the studies, with facilitation only present in animals that

were anesthetized with barbiturates. Taken together with our findings, it seems that barbiturate

anesthetics have a profound impact on dLGN firing.

The mechanisms of action for barbiturates is through activation of the GABAA receptor.

By increasing inhibitory tone throughout the brain, the spontaneous activity in dLGN appears to

be blocked, either through the activation of local inhibition within or projecting to the dLGN, or

by blocking excitatory inputs into dLGN. In the absence of spontaneous activity, visual input is

more likely to drive bursts, presumably because T-Type Ca++

are more easily deinactivated.

Additionally, without spontaneous dLGN activity the tonic depression at the TC synapse is

lifted.

Our findings suggest that studies of dLGN activity, TC transmission, and experience-

dependent cortical plasticity should be avoided in animals under barbiturate anesthesia, and

when they are the results should be evaluated with caution. Our results should encourage other

researchers to record activity from the awake animal whenever possible.

Future Experiments

Even though there are several important implications to the work we’ve already

completed, there are several potential future experiments that would enhance and refine the data

collected in this thesis. Some avenues of future research would require new experimental

designs, and other could potentially be investigated using the data already recorded. Several

potential ideas are described below.

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Cortical Activity during Visual Deprivation

Our studies describe the activity in dLGN during MC and MI. However, the plasticity

that occurs in cortex depends on cortical activity patterns in addition to the presynaptic input.

Therefore, to fully understand the mechanisms that contribute to OD plasticity, it is necessary to

record cortical activity during deprivation.

The “ideal” experimental protocol would consist of chronically implanted recording

electrodes in both dLGN and layer IV of the binocular segment of V1 with electrodes recording

from neurons that are monosynaptically connected. This would require mapping the receptive

fields of the dLGN and V1 neurons during electrode placement and performing cross-correlation

analysis to verify connectivity (Tanaka, 1983). Once the electrodes were positioned to record the

connected neuron pairs, they would be affixed to the animal with dental acrylic. Following a

baseline recording session and either MC or MI, subsequent recording sessions could be

conducted throughout the period of deprivation.

This experimental design would accomplish several goals. First, we would increase the

data set for chronic recordings from the dLGN during MI, as well as establish a data set of

chronic recordings during MC. Additionally, we would record data sets of chronic recordings

from V1 during both MC and MI. Finally, the most important advantage to this design would be

to follow the correlations between monosynaptically connected pairs of dLGN and V1 neurons

before and during extended periods of MC and MI. Furthermore, the data obtained from pairs

during baseline may elucidate properties of TC transmission in the awake mouse.

This experiment is particularly difficult in practice, due to the difficulty of finding

monosynaptically connected pairs of neurons (Alonso et al., 2001) and holding them for chronic

recording. Several modifications of the experiment may be easier and will still yield useful data

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sets. One option would be to attempt the paired recordings in the acute preparation. While this

eliminates the ability to record from the neurons during extended periods of deprivation,

connected pairs are less likely to be lost during the cementing process of the chronic preparation.

However, using microdrives might overcome this difficulty. Another option would be to record

chronically (or acutely) without specifically searching for connected pairs, while a final option

would be to record only from V1.

We would hypothesize that during MC, activity between pairs of dLGN and V1 neurons

would show a decrease in correlative firing, which may be accompanied by an overall decrease

in cortical activity. These conditions would support LTD under a Hebbian or BCM-like model

of synaptic plasticity. It is much more difficult to speculate on the cortical activity following MI.

The cortex may be quiet due to increased drive on inhibitory neurons (described above).

Furthermore, studies of immediate early gene (IEG) expression in V1 following MI show a

reduction in zif268 but not cJun expression in the hemisphere of V1 contralateral to the injected

eye in rodents (Worley et al., 1991). While it is difficult to interpret these results, zif268

expression is related to NMDAR activation (Worley et al., 1991) and may be related to synaptic

plasticity, while cJun activity is more indicative of overall activity. This implies that MI may not

have a strong effect on overall cortical activity, but may decrease the amount of plasticity in V1.

This fits with the finding that there is no deprived-eye depression following MI (Frenkel et al.,

2006). Even if simultaneous dLGN and V1 recordings are not feasible, looking at the V1

activity alone may be sufficient to resolve these questions and understand the effects of MI and

dLGN bursting. While recordings from paired dLGN and V1 neurons would provide the most

informative way to evaluate the effects of dLGN bursting on V1 activity, even recordings from

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dLGN and V1 neurons that are not connected would be useful since bursting activity within the

dLGN is correlated (Chapter 3).

Recordings from the Monocular Zone of the dLGN

The dLGN is divided into a binocular zone which contains neurons that project to the

binocular region of V1, and a monocular zone, containing neurons that project to the monocular

region of V1. Each region of the dLGN receives cortical feedback from the region of V1 it

projects to. Therefore, if binocular and monocular V1 change differently following MC and/or

MI, they may have distinct effects of dLGN firing.

There is no change in VEP amplitude in the monocular zone following MC, presumably

due to the lack of interaction between deprived and non-deprived eye inputs in the monocular

zone (unpublished data, Frenkel and Bear). However, recent studies have shown increased

spontaneous firing rate and changes in mEPSC amplitude following MC and MI in slices of

monocular V1 studied ex vivo (Maffei and Turrigiano, 2008) and increased evoked response as

measured with calcium imaging in vivo (Mrsic-Flogel et al., 2007), suggesting plastic changes

occur in this region. Even if plastic changes are occurring in the monocular zone, they may be

different from those in the binocular zone, thus the feedback these two brain regions send to the

two segments of dLGN may differ.

Our work is focused on changes that lead to ocular dominance plasticity in the binocular

region of V1, therefore we are primarily interested in neurons recorded from the binocular zone

of the dLGN. Because of the curved shape of the dLGN, one is more likely to penetrate the

binocular zone of the dLGN when inserting an electrode from the dorsal surface. However,

inserting electrodes at the appropriate angle of penetration would facilitate recordings from the

monocular zone.

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Monocular zone recordings may be particularly interesting because the different changes

between binocular and monocular V1 will lead to different feedback into the two zones of

dLGN. Studies of the differences between the binocular and monocular zones of the dLGN may

elucidate how much of the differences in spike patterns following MC and MI were due to feed-

forward, as opposed to feedback inputs.

Details of dLGN Bursting in the Absence of Visual Input

It would be interesting to determine which brain areas are contributing to the increased

bursting during MI. Potential input sources include cortical feedback, the thalamic reticular

nucleus (TRN), and brain stem input. Inactivating one or more of these regions may help

elucidate the source of the bursts.

In addition to understanding the source of the bursting input, we would like to understand

the purpose of the bursts. One way to address the effects of MI in the absence of bursting is to

study OD plasticity in mice with the T-Type Ca++

channel knocked-out (KO) in the thalamus

(Anderson et al., 2005). One hypothesis would be that the KO mice would not show potentiation

of the non-deprived eye in the absence of bursting in the ipsilateral core. However, because

sleep is disrupted in these animals (Anderson et al., 2005), plasticity may be affected by that as

well.

dLGN Activity in the Freely Behaving Animal, Particularly during Sleep

In typical experiments studying OD plasticity, the animals are returned to their home

cage during the period of visual deprivation. To best capture the activity patterns that result in

OD plasticity, we would want to observe dLGN activity while the animal is freely behaving in

his home cage. The behavioral state of the animal will affect the dLGN firing activity, and it is

possible that by restricting ourselves to recording from head-fixed animals, we are missing

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behavioral states where activity during MC and MI may differ from normal viewing in different

ways than those we observed in the present study. To monitor behavioral state in freely

behaving animals we would need to implant hippocampal or cortical EEG electrodes and nuchal

muscle EMG electrodes in addition to our electrode bundle in dLGN.

Studying dLGN activity during sleep may also be informative in terms of understanding

activity patterns that contribute to OD plasticity. Sleep has been shown to enhance the effects of

OD plasticity in cats (Frank et al., 2001). Furthermore, non-rapid eye movement (NREM) sleep

has been shown to enhance LTD (reviewed in Benington and Frank, 2003). Because deprived-

eye depression utilizes LTD-like mechanisms (Heynen et al., 2003), taken together these

findings suggest that activity during NREM sleep may significantly contribute to deprived-eye

depression in vivo. Additionally, NREM activity is dominated by bursting activity, and we

observed a decrease in bursting following MC in adult animals (but not juveniles) and an

increase in bursting following MI. Perhaps bursting activity in the awake animal affects bursting

during sleep and contributes to the overall effects of visual deprivation.

Plasticity in the dLGN

As described in Chapter 1, there is very little evidence for plasticity at the

retinogeniculate synapse following MC (Wiesel and Hubel, 1963; Sesma et al., 1984; Levitt et

al., 2001). However, using a chronic recording preparation, we could test for plasticity in the

dLGN of the mouse. Recordings following eyelid opening after MC or recover from MI would

allow us to test for changes in visual responsiveness.

Verification of Homosynaptic LTD in V1

In homosynaptic LTD, active synapses depress due to their own activity patterns while in

heterosynaptic LTD, inactive synapse depress due to higher activity in other synapses (Bear and

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Abraham, 1996; Blais et al., 1999) (described above). In our study, we found that depression

occurs during MC, not due to a decrease in activity, but due to a decrease in correlation of the

deprived-eye input. While our findings suggest that this de-correlation itself contributes to the

depression of those synapses, the definitive study to distinguish homo- and hetero-synaptic LTD

requires silencing one input pattern. If the silenced input does not depress, you can conclusively

determine that the depression uses homosynaptic mechanisms.

This was the intent of the Rittenhouse et al. (1999) study. However, this study assumed

that by silencing the retina, dLGN would be silent as well, and this assumption was also used in

the theoretical modeling work which led to the conclusion that a homosynaptic learning rule

drove deprived-eye depression (Blais et al., 1999). Because we now know that MI does not

silence dLGN activity, it would be informative to conduct an experiment where the contralateral

shell of the dLGN is silenced. Silencing only the contralateral segment of the dLGN is

nontrivial. One potential approach may be through expression of the ivermectin (IVM)-gated

Cl− channel (GluCl) (reviewed inLuo et al., 2008). GluCl channels require the expression of two

subunits to be functional. One could imagine a system where one subunit is expressed

throughout the brain, but the other subunit is introduced via intraocular delivery of a virus which

travels in an anterograde fashion, restricted to shell of the contralateral dLGN. This region can

then be silenced by intracranial delivery of IVM. VEP recordings from these animals could

confirm that the silenced input does not lead to depression, confirming that the plasticity is

homosynaptic.

Physiologically Relevant LTD Induction Protocols

LTP and LTD are popluar for synaptic plasticity in the brain. However, the induction

protocols for these models are rarely physiologically relevant (reviewed in Holscher, 1999; and

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Albensi et al., 2007). Because the OD plasticity resulting from MC is known to mimic and

occlude LTD (Heynen et al., 2003), our recordings of dLGN activity during MC represent the

actual presynaptic activity patterns that drive plasticity in vivo. Therefore, a “playback” of this

activity in a slice preparation should induce LTD in vitro.

Initially this experiment could be attempted in the IV to II/III synapse in visual cortex

because of the ease of making visual cortical slices. However, the Yuste laboratory has recently

developed a visual thalamocortical slice preparation (MacLean et al., 2006). Using this

preparation one could “playback” our data into the contralateral shell of the dLGN. The activity

recorded during MC should result in LTD in cortex, while the activity recorded during normal

viewing and MI should not. This new LTD induction protocol should better approximate the

activity patterns that drive plasticity in vivo and using this protocol in vitro may provide new

insight into the mechanisms of cortical plasticity.

Other Visual Manipulations

It may be informative to record from the dLGN (and ideally simultaneously from V1)

during other forms of visual deprivation including binocular lid closure (BC), binocular retinal

inactivation (BI), and complete darkness (DE). While the activity patterns during BC and BI

may not be different in dLGN, cortical activity is most likely different under these conditions,

thus cortical feedback to dLGN neurons may be affected. Also, the DE condition would serve as

a comparison to MC, as discussed above.

Secondary Verification

A final series of future experiments would be experiments to confirm our findings. Using

extracellular recordings, we have shown that MC and MI do not reduce the overall firing rates in

dLGN. Because the results were surprising and somewhat unexpected, we would like to verify

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this through an independent method. In addition to electrophysiological recordings, there are

molecular markers that one can use to monitor neural activity at the cellular level. Under

specific conditions, the expression of IEGs can be used as such a marker. The IEG cFos, a

transcription factor, one marker for neural activity that is expressed in dLGN and V1 (Herdegen

et al., 1998). Thus, we may be able to confirm the electrophysiological recordings by monitoring

levels of cFos expression during visual deprivation.

Basal cFos levels are low in both dLGN and V1 (reviewed in Herdegen et al., 1998).

However, cFos expression can be induced by introducing visual stimuli after a period of dark

exposure (reviewed in Kaczmarek and Chaudhuri, 1997). Therefore, in our proposed

experiments, after an extended period of dark exposure, mice would be exposed to a period of (1)

light, (2) light with MC, (3) light with MI, or (4) MI in the dark. Immunofluorescent staining

and confocal imaging would allow us to visualize and quantify cFos expression at the cellular

level. Next, we would make comparisons both within the same animal between its two

hemispheres (which receive different visual inputs) and between animals with different visual

histories. If dLGN activity levels are the same during MC, MI and light conditions, we would

expect all of these conditions to show higher levels of cFos expression than the dark conditions.

We could also use this technique to examine activity levels in V1. Prior to these experiments, it

may be necessary to confirm that dLGN firing patterns are the same as in the present study when

assessed following a period of dark exposure.

We have performed an extensive pilot study, but, unfortunately the quality of cFos

staining has been variable and unreliable. An alternative approach for visualizing cFos activity

would be to use transgenic mice that harbor a fos-GFP fusion gene under the control of the

native fos promoter (Barth, 2007). The mice would be exposed to the same conditions as

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described above, and visualization of GFP levels could be used to determine the level of cFos

activation.

More Advanced Spike-Train Analysis

In addition to the experiments proposed above, there are some studies that can possibly

be done by applying new analyses to the data set. Examinations of the raster plots and peri-

stimulus time histograms for activity recorded during MC do not seem to indicate that there is an

effect of visual input. However, no careful analysis has been applied to the spike trains. One

possibility would be to apply a generalized linear model (GLM) to the data (Truccolo et al.,

2005). This technique models the spike train as a point-process and models the relative effects

of spiking history, simultaneously recorded spike trains, and extrinsic factors like visual input. If

the visual input was a significant factor in the model, we could conclude that the neuron was

responding to visual stimuli through the eyelid.

Recent advances in GLM methodology now allow for the analysis of between-trial

variability of spike trains (Czanner et al., 2008). We did not examine the response to the

sinusoidal gratings on a trial-by-trial basis. It may be possible that one difference between

viewing and MC is the amount of trial-to-trial variability. Additionally, we may find short term-

plasticity effects in the dLGN, such as an increased response to repeated stimuli, if we evaluated

the data in this fashion.

Conclusions

In conclusion, we have provided the first recordings of dLGN activity that leads to OD

plasticity in the awake animal. Characterizing this activity can allow for new ways to study

experience-dependent cortical plasticity. This will lead to a more complete understanding of the

mechanisms that allow for these changes. We have also discovered that dLGN activity bursts

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following an elimination of retinal input. Bursting does not occur following MI in the

anesthetized animal, underscoring the importance of recording from the awake preparation.

These findings allow for new interpretations of old studies, and should guide future research in

cortical plasticity. Furthermore, the results may prove invaluable in the treatments of

developmental disorders including ambylopia.

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Appendix

1. DETAILED METHODS FOR dLGN RECORDINGS

2. RESPONSE TO CONCERNS

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Appendix 1 – Detailed Methods for dLGN Recordings

Surgical Preparation

Prior to surgery:

For each surgery, you will need 1 head post, 1 plastic ring, 2 “gold pin” electrodes, and 1 “plumb

line.”

To make the head post, cut a ¾” x 18 wire nail to 1 cm and smooth the cut edge with the dremel.

To make the plastic ring, cut a ring from a 100 – 1000 μL pipette tip using a razor blade.

To make the electrodes, solder a small length of 0.01” diameter bare silver wire into a male gold

wire contact.

To make the plumb line, remove the sharp point and plastic portion of a 30g needle, leaving just

the needle shaft.

Materials

Needles (26g and 30g) and 1 mL syringes: 1 syringe plus 1 26g needle for anesthesia. 1 syringe

plus 1 26g needle for lidocaine. 1 syringe plus 1 30g needle for Zip Kicker. Additional 30g

needles for glue application.

Ketamine HCl, Ketaject

Xylazine, Xylaject

Lidocaine HCl 2%

Petrolatum Ophthalmic Ointment, Puralube Vet Ointment

70% EtOH

Alcohol Swabs

Iodine Swabs

“Zip Kicker”

Loctite Glue #495

Loctite Glue #454

Ortho Jet Acrylic Jet powder

Ortho Jet Acrylic Liquid

Electric razor

Scissors

Razor blade

Q-tips

Air duster

Forceps

Hex drivers for stereotaxic apparatus

Skin marker

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Rechargeable High Speed Microdrill

Drill bit (0.7 mm)

Wire cutters

Spatula

Aluminum Dish (for Ortho Jet Arcylic)

Methods

Anesthetize the mouse with a cocktail of 50 mg/kg ketamine and 10 mg/kg xylazine ip.

Shave the fur on the mouse head removing as much as possible from the snout back past the ears,

getting as close to the eyes as possible. Avoid whiskers.

Clean the surgical area with iodine and alcohol (x3). Apply ophthalmic ointment to the eyes.

Inject 0.1 mL of lidocaine subcutaneously above the skull.

Using scissors and forceps, cut open the skin over the skull.

Clean the skull surface with q-tips.

Use a razor blade to roughen the surface of the skull. Clean the surface with EtOh. Dry

thoroughly with air duster.

Using the 495 glue, affix the head post anterior to bregma (on the frontal suture). Solidify glue

with zip kicker.

Place the mouse into the stereotaxic apparatus. Tighten head post with hex driver. Level the

skull, and ensure the midline is straight.

Reinforce head post with 454 glue and zip kicker.

Using the skin marker, mark the location of 2.1 posterior of bregma, 2.2 lateral of midline.

Using 454 glue and zip kicker, affix the plastic ring around this point.

Trim gold pin electrodes with wire cutters. Place the two gold pin electrodes in the hemisphere

opposite the plastic ring by thinning the skull with the microdrill at the insertion location,

pushing the pin through the skull, and affixing in place with 454 glue and zip kicker. The

reference electrode should be over frontal cortex, and the EEG electrode should be over occipital

cortex (on lambda).

Using the stereotaxic electrode holder to hold the plumb line, glue the plumb line slightly

posterior to the plastic ring using 454 glue and zip kicker.

Mix the Ortho Jet acrylic powder and liquid in an aluminum dish. Cover all surfaces outside of

the plastic ring with Ortho Jet acrylic using the spatula.

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Habituation

Animals are placed in the recording apparatus and head fixed for at least 3 30+ minute sessions,

while the animals views a gray screen on the monitor. During the first session no occluder is

used. During the second and third session, one eye, then the other is occluded.

Recording

Materials

Electrode bundle

Isoflurane

Saline

Microdrill

Drill bit (0.7 mm)

30g needle

Air duster

Surgical Eye Spears

Absorbent paper points

Methods

Animals are placed in the recording apparatus and anesthetized with 1.5-3% Isoflurane in 100%

oxygen.

Using the microdrill, perform a small craniotomy at the site of the marker. A 30g needle can be

used to remove sharp edges from the craniotomy. Remove bone particles with the air duster.

Clean the area and stop any bleeding with saline, surgical eye spears and paper points.

Remove animal from Isoflurane.

Align the electrode bundle to plumb line. Slowly lower electrode bundle into the brain. Be

aware of dimpling. Record during electrode penetration to hear cortex and hippocampus. Once

past the hippocampus, dimpling may occur again at thalamic surface. Listen for response to

sinusoidal grating.

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Appendix 2 – Response to Concerns

Here I will provide detailed responses to two concerns of my thesis committee: the

possible correlation between behavioral state and increased correlation following MI, and the

sharp peaks observed when cross-correlation analysis was performed.

Behavioral State and Bursting

Certain behavioral states are correlated with an increase in burst percentage (e.g.

Bezdudnaya et al., 2006). Because we saw an increase in burst percentage following MI, it is

necessary to confirm that this increase was not due to a change in behavioral state. To address

this concern, we analyzed the cortical EEG recorded from 3 mice in the control and MI groups.

Power spectral analysis of EEG recorded from occipital cortex was performed in 10 s bins using

the Welch method of spectral estimation. We defined the delta band as frequencies below 4 Hz

and the theta band as frequencies between 4.5 and 9 Hz. We used the theta /delta ratio (TDR) as

our measure of behavioral state (Maloney et al., 1997).

Our results are summarized in Figure A-1. Fig. A-1a shows the mean TDRs before and

after manipulation in the control and MI groups. First, the average TDR was calculated across

all 10 s bins for each animal in each condition. Bars represent the mean over the 3 averages from

each group. There are no differences between groups (p > 0.5, Kruskal-Wallis). This suggests

there is no change in behavioral state following MI. Fig. A-1b,c show the relationship between

the TDR in each 10 s bin to the number of bursts recorded during that bin from each neuron

recorded from the animals. While we do see a peak in bursts at TDRs around 1, possibly

representing an inattentive state, there is no clear correlation between TDR and the number of

observed bursts. For example, high levels of bursting occur when the TDR is high, indicating

151

that bursts can be observed when the animal is aroused. Furthermore, there is no evidence that

MI affects the TDRs observed, nor does it affect the relationship between TDR and bursting.

Sharp Correlogram Peaks

Alonso et al. (1996) have reported dLGN neurons with highly synchronous firing.

Neurons correlated at the 1 ms timescale were recorded from electrodes 100 – 400 μm apart and

had very similar, overlapping receptive fields. When neurons had overlapping receptive fields,

they showed correlations on average of about 28% (Alonso et al., 1996).

We analyzed the sharp peaks in our correlograms to make the case that we were not

recording from the same neurons on multiple electrodes. Correlograms were originally

constructed using 1 ms bins. For those that were highly peaked, we reconstructed the

correlogram using .1 ms bins. Similar to the Alonso et al. study, we found an average correlation

of 22%. Because our electrodes are closely bundled together, and because rodent receptive fields

are larger than those in the cat, we would suspect our neurons to have overlapping fields, so

these results should not be surprising.

Furthermore, when we considered the correlations after MC and MI, we found that the

large majority of neurons that were tightly correlated (in .1 ms bins) prior to manipulation, no

longer showed a tight correlation at the .1 ms timescale following either manipulation (10 out of

12 neuron pairs). If we were in fact recording from the same neuron on two electrodes, the

amount of correlative firing observed should not be affected by visual manipulation. This

suggests that the observed peaks were related to driven retinal ganglion cell input, which is

disrupted by MC and MI. In the control group however, 8 of the 11 neuron pairs remained

tightly correlated following anesthesia, again suggesting convergent retinal ganglion cell input.

2 pairs in the MI group that were not tightly correlated at the .1 ms level became tightly

152

correlated following MI. There are no examples of this in the MC or the control group,

suggesting that this occurred as a result of the TTX. (The example in Fig. 2-8a, right panel, was

one of these pairs. Prior to MI, the tight correlation observed at the 1 ms bin size was not

observed at the .1 ms bin size, however we did observe this following MI.)

However, we acknowledge that it still possible that we were recording spikes from the

same neuron on more than one electrode (particularly in the 2 of 12 pairs that did not change

after manipulation). While this would have a small impact on our data sets, we do not believe it

would change our overall conclusions.

153

Figure A-1

Figure A-1 Increased bursting is not associated with a change in behavioral state.

a, Mean theta/delta ratios from 3 animals in the control group (left) and the MI group (right).

White bars are the baseline or (O)pen eye condition. Black bars are the post-manipulation

conditions of (O)pen eye for the control group and (I)nactivated eye for the MI group. Power-

spectral analysis was performed in 10 s bins across the entire recording session and averaged for

each animal. Bars represent the means of the three animals; error bars show standard error.

There are no differences in theta/delta ratio (p > 0.5, Kruskal-Wallis). b – c, Scatter plots of

theta/delta ratio vs the number of bursts. Each circle represents a single 10 s bin over which

power-spectral analysis was performed for the animal and the number of bursts were counted for

the unit. Open circles represent data from the baseline condition and filled gray circles are the

post-manipulation conditions of (b) open eye viewing and (c) MI. Data represent (b) 9 neurons

from 3 animals and (c) 10 neurons from 3 animals. While bursting peaks at a theta/delta ratio

around 1, there is no clear correlation between theta/delta ratio and the amount of bursts, nor is

there an apparent effect of MI.

155

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